Presentation is loading. Please wait.

Presentation is loading. Please wait.

Machine Learning https://store.theartofservice.com/the-machine-learning-toolkit.html.

Similar presentations


Presentation on theme: "Machine Learning https://store.theartofservice.com/the-machine-learning-toolkit.html."— Presentation transcript:

1 Machine Learning https://store.theartofservice.com/the-machine-learning-toolkit.html

2 Predictive analytics Machine learning techniques 1 For such cases, machine learning techniques emulate human cognition and learn from training examples to predict future events. https://store.theartofservice.com/the-machine-learning-toolkit.html

3 Predictive analytics Machine learning techniques 1 A brief discussion of some of these methods used commonly for predictive analytics is provided below. A detailed study of machine learning can be found in Mitchell (1997). https://store.theartofservice.com/the-machine-learning-toolkit.html

4 Decentralized Autonomous Corporation - Machine learning layer 1 This layer runs the Artificial Intelligence algorithm that the DAC relies on to detect patterns in real-world data and model it without human intervention. https://store.theartofservice.com/the-machine-learning-toolkit.html

5 Machine learning 1 Machine learning, a branch of Artificial Intelligence, concerns the construction and study of systems that can learn from data. For example, a machine learning system could be trained on messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new messages into spam and non-spam folders. https://store.theartofservice.com/the-machine-learning-toolkit.html

6 Machine learning 1 The core of machine learning deals with representation and generalization. Representation of data instances and functions evaluated on these instances are part of all machine learning systems. Generalization is the property that the system will perform well on unseen data instances; the conditions under which this can be guaranteed are a key object of study in the subfield of computational learning theory. https://store.theartofservice.com/the-machine-learning-toolkit.html

7 Machine learning 1 There is a wide variety of machine learning tasks and successful applications. Optical character recognition, in which printed characters are recognized automatically based on previous examples, is a classic example of machine learning. https://store.theartofservice.com/the-machine-learning-toolkit.html

8 Machine learning - Definition 1 In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". https://store.theartofservice.com/the-machine-learning-toolkit.html

9 Machine learning - Definition 1 This definition is notable for its defining machine learning in fundamentally operational rather than cognitive terms, thus following Alan Turing's proposal in Turing's paper "Computing Machinery and Intelligence" that the question "Can machines think?" be replaced with the question "Can machines do what we (as thinking entities) can do?" https://store.theartofservice.com/the-machine-learning-toolkit.html

10 Machine learning - Generalization 1 A core objective of a learner is to generalize from its experience https://store.theartofservice.com/the-machine-learning-toolkit.html

11 Machine learning - Machine learning and data mining 1 These two terms are commonly confused, as they often employ the same methods and overlap significantly. They can be roughly defined as follows: https://store.theartofservice.com/the-machine-learning-toolkit.html

12 Machine learning - Machine learning and data mining 1 Machine learning focuses on prediction, based on known properties learned from the training data. https://store.theartofservice.com/the-machine-learning-toolkit.html

13 Machine learning - Machine learning and data mining 1 Data mining focuses on the discovery of (previously) unknown properties in the data. This is the analysis step of Knowledge Discovery in Databases. https://store.theartofservice.com/the-machine-learning-toolkit.html

14 Machine learning - Machine learning and data mining 1 Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in Knowledge Discovery and Data Mining (KDD) the key task is the discovery of previously unknown knowledge https://store.theartofservice.com/the-machine-learning-toolkit.html

15 Machine learning - Human interaction 1 Some machine learning systems attempt to eliminate the need for human intuition in data analysis, while others adopt a collaborative approach between human and machine. Human intuition cannot, however, be entirely eliminated, since the system's designer must specify how the data is to be represented and what mechanisms will be used to search for a characterization of the data. https://store.theartofservice.com/the-machine-learning-toolkit.html

16 Machine learning - Algorithm types 1 Machine learning algorithms can be organized into a taxonomy based on the desired outcome of the algorithm or the type of input available during training the machine. https://store.theartofservice.com/the-machine-learning-toolkit.html

17 Machine learning - Algorithm types 1 Supervised learning algorithms are trained on labelled examples, i.e., input where the desired output is known. The supervised learning algorithm attempts to generalise a function or mapping from inputs to outputs which can then be used to speculatively generate an output for previously unseen inputs. https://store.theartofservice.com/the-machine-learning-toolkit.html

18 Machine learning - Algorithm types 1 Unsupervised learning algorithms operate on unlabelled examples, i.e., input where the desired output is unknown. Here the objective is to discover structure in the data (e.g. through a cluster analysis), not to generalise a mapping from inputs to outputs. https://store.theartofservice.com/the-machine-learning-toolkit.html

19 Machine learning - Algorithm types 1 Semi-supervised learning combines both labeled and unlabelled examples to generate an appropriate function or classifier. https://store.theartofservice.com/the-machine-learning-toolkit.html

20 Machine learning - Algorithm types 1 Transduction, or transductive inference, tries to predict new outputs on specific and fixed (test) cases from observed, specific (training) cases. https://store.theartofservice.com/the-machine-learning-toolkit.html

21 Machine learning - Algorithm types 1 Reinforcement learning is concerned with how intelligent agents ought to act in an environment to maximise some notion of reward. The agent executes actions which cause the observable state of the environment to change. Through a sequence of actions, the agent attempts to gather knowledge about how the environment responds to its actions, and attempts to synthesise a sequence of actions that maximises a cumulative reward. https://store.theartofservice.com/the-machine-learning-toolkit.html

22 Machine learning - Algorithm types 1 Learning to learn learns its own inductive bias based on previous experience. https://store.theartofservice.com/the-machine-learning-toolkit.html

23 Machine learning - Algorithm types 1 Developmental learning, elaborated for Robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers, and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation. https://store.theartofservice.com/the-machine-learning-toolkit.html

24 Machine learning - Theory 1 The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. https://store.theartofservice.com/the-machine-learning-toolkit.html

25 Machine learning - Theory 1 In addition to performance bounds, computational learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time. https://store.theartofservice.com/the-machine-learning-toolkit.html

26 Machine learning - Theory 1 There are many similarities between machine learning theory and statistical inference, although they use different terms. https://store.theartofservice.com/the-machine-learning-toolkit.html

27 Machine learning - Decision tree learning 1 Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value. https://store.theartofservice.com/the-machine-learning-toolkit.html

28 Machine learning - Association rule learning 1 Association rule learning https://store.theartofservice.com/the-machine-learning-toolkit.html

29 Machine learning - Association rule learning 1 Association rule learning is a method for discovering interesting relations between variables in large databases. https://store.theartofservice.com/the-machine-learning-toolkit.html

30 Machine learning - Artificial neural networks 1 artificial neural network https://store.theartofservice.com/the-machine-learning-toolkit.html

31 Machine learning - Artificial neural networks 1 An artificial neural network (ANN) learning algorithm, usually called "neural network" (NN), is a learning algorithm that is inspired by the structure and functional aspects of biological neural networks https://store.theartofservice.com/the-machine-learning-toolkit.html

32 Machine learning - Inductive logic programming 1 Inductive logic programming https://store.theartofservice.com/the-machine-learning-toolkit.html

33 Machine learning - Inductive logic programming 1 Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program which entails all the positive and none of the negative examples. https://store.theartofservice.com/the-machine-learning-toolkit.html

34 Machine learning - Support vector machines 1 Support vector machines https://store.theartofservice.com/the-machine-learning-toolkit.html

35 Machine learning - Support vector machines 1 Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. https://store.theartofservice.com/the-machine-learning-toolkit.html

36 Machine learning - Clustering 1 Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to some predesignated criterion or criteria, while observations drawn from different clusters are dissimilar https://store.theartofservice.com/the-machine-learning-toolkit.html

37 Machine learning - Bayesian networks 1 A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG) https://store.theartofservice.com/the-machine-learning-toolkit.html

38 Machine learning - Reinforcement learning 1 Reinforcement learning is concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. Reinforcement learning algorithms attempt to find a policy that maps states of the world to the actions the agent ought to take in those states. Reinforcement learning differs from the supervised learning problem in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. https://store.theartofservice.com/the-machine-learning-toolkit.html

39 Machine learning - Representation learning 1 Several learning algorithms, mostly unsupervised learning algorithms, aim at discovering better representations of the inputs provided during training https://store.theartofservice.com/the-machine-learning-toolkit.html

40 Machine learning - Similarity and metric learning 1 In this problem, the learning machine is given pairs of examples that are considered similar and pairs of less similar objects. It then needs to learn a similarity function (or a distance metric function) that can predict if new objects are similar. It is sometimes used in Recommendation systems. https://store.theartofservice.com/the-machine-learning-toolkit.html

41 Machine learning - Sparse Dictionary Learning 1 In this method, a datum is represented as a linear combination of basis functions, and the coefficients are assumed to be sparse. Let x be a d-dimensional datum, D be a d by n matrix, where each column of D represents a basis function. r is the coefficient to represent x using D. Mathematically, sparse dictionary learning means the following where r is sparse. Generally speaking, n is assumed to be larger than d to allow the freedom for a sparse representation. https://store.theartofservice.com/the-machine-learning-toolkit.html

42 Machine learning - Sparse Dictionary Learning 1 Sparse dictionary learning has been applied in several contexts https://store.theartofservice.com/the-machine-learning-toolkit.html

43 Machine learning - Applications 1 Applications for machine learning include: https://store.theartofservice.com/the-machine-learning-toolkit.html

44 Machine learning - Applications 1 In 2006, the online movie company Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million. https://store.theartofservice.com/the-machine-learning-toolkit.html

45 Machine learning - Applications 1 In 2010 The Wall Street Journal wrote about a money management firm Rebellion Research's use of machine learning to predict economic movements, the article talks about Rebellion Research's prediction of the financial crisis and economic recovery. https://store.theartofservice.com/the-machine-learning-toolkit.html

46 Machine learning - Software 1 Ayasdi, Angoss KnowledgeSTUDIO, Apache Mahout, Gesture Recognition Toolkit, IBM SPSS Modeler, KNIME, KXEN Modeler, LIONsolver, MATLAB, mlpy, MCMLL, OpenCV, dlib, Oracle Data Mining, Orange, Python scikit-learn, R, RapidMiner, Salford Predictive Modeler, SAS Enterprise Miner, Shogun toolbox, STATISTICA Data Miner, and Weka are software suites containing a variety of machine learning algorithms. https://store.theartofservice.com/the-machine-learning-toolkit.html

47 Machine learning - Journals and conferences 1 Journal of Machine Learning Research https://store.theartofservice.com/the-machine-learning-toolkit.html

48 Machine learning - Journals and conferences 1 Neural Computation (journal) https://store.theartofservice.com/the-machine-learning-toolkit.html

49 Machine learning - Journals and conferences 1 Journal of Intelligent Systems(journal) https://store.theartofservice.com/the-machine-learning-toolkit.html

50 Machine learning - Journals and conferences 1 Neural Information Processing Systems (NIPS) (conference) https://store.theartofservice.com/the-machine-learning-toolkit.html

51 Machine learning - Further reading 1 Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012). Foundations of Machine Learning, The MIT Press. ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

52 Machine learning - Further reading 1 Ian H. Witten and Eibe Frank (2011). Data Mining: Practical machine learning tools and techniques Morgan Kaufmann, 664pp., ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

53 Machine learning - Further reading 1 Sergios Theodoridis, Konstantinos Koutroumbas (2009) "Pattern Recognition", 4th Edition, Academic Press, ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

54 Machine learning - Further reading 1 Mierswa, Ingo and Wurst, Michael and Klinkenberg, Ralf and Scholz, Martin and Euler, Timm: YALE: Rapid Prototyping for Complex Data Mining Tasks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06), https://store.theartofservice.com/the-machine-learning-toolkit.html

55 Machine learning - Further reading 1 Bing Liu (2007), Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

56 Machine learning - Further reading 1 Huang T.-M., Kecman V., Kopriva I. (2006), Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning, Springer-Verlag, Berlin, Heidelberg, 260 pp. 96 illus., Hardcover, ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

57 Machine learning - Further reading 1 Ethem Alpaydın (2004) Introduction to Machine Learning (Adaptive Computation and Machine Learning), MIT Press, ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

58 Machine learning - Further reading 1 MacKay, D.J.C. (2003). Information Theory, Inference, and Learning Algorithms, Cambridge University Press. ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

59 Machine learning - Further reading 1 KECMAN Vojislav (2001), Learning and Soft Computing, Support Vector Machines, Neural Networks and Fuzzy Logic Models, The MIT Press, Cambridge, MA, 608 pp., 268 illus., ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

60 Machine learning - Further reading 1 Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

61 Machine learning - Further reading 1 Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

62 Machine learning - Further reading 1 Ryszard S. Michalski, George Tecuci (1994), Machine Learning: A Multistrategy Approach, Volume IV, Morgan Kaufmann, ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

63 Machine learning - Further reading 1 Sholom Weiss and Casimir Kulikowski (1991). Computer Systems That Learn, Morgan Kaufmann. ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

64 Machine learning - Further reading 1 Yves Kodratoff, Ryszard S. Michalski (1990), Machine Learning: An Artificial Intelligence Approach, Volume III, Morgan Kaufmann, ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

65 Machine learning - Further reading 1 Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1986), Machine Learning: An Artificial Intelligence Approach, Volume II, Morgan Kaufmann, ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

66 Machine learning - Further reading 1 Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1983), Machine Learning: An Artificial Intelligence Approach, Tioga Publishing Company, ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

67 Machine learning - Further reading 1 Ray Solomonoff, An Inductive Inference Machine, IRE Convention Record, Section on Information Theory, Part 2, pp., 56-62, https://store.theartofservice.com/the-machine-learning-toolkit.html

68 Machine learning - Further reading 1 Ray Solomonoff, "An Inductive Inference Machine" A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI. https://store.theartofservice.com/the-machine-learning-toolkit.html

69 Natural language processing - NLP using machine learning 1 The paradigm of machine learning is different from that of most prior attempts at language processing https://store.theartofservice.com/the-machine-learning-toolkit.html

70 Natural language processing - NLP using machine learning 1 Many different classes of machine learning algorithms have been applied to NLP tasks https://store.theartofservice.com/the-machine-learning-toolkit.html

71 Natural language processing - NLP using machine learning 1 Systems based on machine-learning algorithms have many advantages over hand-produced rules: https://store.theartofservice.com/the-machine-learning-toolkit.html

72 Natural language processing - NLP using machine learning 1 The learning procedures used during machine learning automatically focus on the most common cases, whereas when writing rules by hand it is often not obvious at all where the effort should be directed. https://store.theartofservice.com/the-machine-learning-toolkit.html

73 Natural language processing - NLP using machine learning 1 Automatic learning procedures can make use of statistical inference algorithms to produce models that are robust to unfamiliar input (e.g. containing words or structures that have not been seen before) and to erroneous input (e.g. with misspelled words or words accidentally omitted). Generally, handling such input gracefully with hand-written rules — or more generally, creating systems of hand-written rules that make soft decisions — is extremely difficult, error-prone and time- consuming. https://store.theartofservice.com/the-machine-learning-toolkit.html

74 Natural language processing - NLP using machine learning 1 Systems based on automatically learning the rules can be made more accurate simply by supplying more input data https://store.theartofservice.com/the-machine-learning-toolkit.html

75 Natural language processing - NLP using machine learning 1 The subfield of NLP devoted to learning approaches is known as Natural Language Learning (NLL) and its conference CoNLL and peak body SIGNLL are sponsored by ACL, recognizing also their links with Computational Linguistics and Language Acquisition. When the aims of computational language learning research is to understand more about human language acquisition, or psycholinguistics, NLL overlaps into the related field of Computational Psycholinguistics. https://store.theartofservice.com/the-machine-learning-toolkit.html

76 Functional decomposition - Machine learning 1 In practical scientific applications, it is almost never possible to achieve perfect functional decomposition because of the incredible complexity of the systems under study. This complexity is manifested in the presence of "noise," which is just a designation for all the unwanted and untraceable influences on our observations. https://store.theartofservice.com/the-machine-learning-toolkit.html

77 Functional decomposition - Machine learning 1 However, while perfect functional decomposition is usually impossible, the spirit lives on in a large number of statistical methods that are equipped to deal with noisy systems https://store.theartofservice.com/the-machine-learning-toolkit.html

78 Functional decomposition - Machine learning 1 As an example, Bayesian network methods attempt to decompose a joint distribution along its causal fault lines, thus "cutting nature at its seams" https://store.theartofservice.com/the-machine-learning-toolkit.html

79 Data compression - Machine learning 1 There is a close connection between machine learning and compression: a system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on the output distribution) while an optimal compressor can be used for prediction (by finding the symbol that compresses best, given the previous history). This equivalence has been used as justification for data compression as a benchmark for general intelligence. https://store.theartofservice.com/the-machine-learning-toolkit.html

80 Self-modifying code - Self-referential machine learning systems 1 Traditional machine learning systems have a fixed, pre-programmed learning algorithm to adjust their parameters. However, since the 1980s Jürgen Schmidhuber has published several self-modifying systems with the ability to change their own learning algorithm. They avoid the danger of catastrophic self-rewrites by making sure that self-modifications will survive only if they are useful according to a user-given fitness function|fitness, error function|error or reward function|reward function. https://store.theartofservice.com/the-machine-learning-toolkit.html

81 Andrew Ng - Machine learning research 1 In 2011, Ng founded the Google Brain project at Google, which developed very large scale artificial neural networks using Google's distributed compute infrastructure. https://store.theartofservice.com/the-machine-learning-toolkit.html

82 Andrew Ng - Machine learning research 1 Among its notable results was a neural network trained using deep learning algorithms on 16,000 CPU cores, that learned to recognize higher-level concepts, such as cats, after watching only YouTube videos, and without ever having been told what a cat is. https://store.theartofservice.com/the-machine-learning-toolkit.html

83 Andrew Ng - Machine learning research 1 The project's technology is currently also used in the Android (Operating System)|Android Operating System's speech recognition system. https://store.theartofservice.com/the-machine-learning-toolkit.html

84 Pattern recognition - Classification (machine learning)|Classification algorithms (supervised learning|supervised algorithms predicting categorical data|categorical labels) 1 Parametric:Assuming known distributional shape of feature distributions per class, such as the Gaussian distribution|Gaussian shape. https://store.theartofservice.com/the-machine-learning-toolkit.html

85 Pattern recognition - Classification (machine learning)|Classification algorithms (supervised learning|supervised algorithms predicting categorical data|categorical labels) 1 *Maximum entropy classifier (aka logistic regression, multinomial logistic regression): Note that logistic regression is an algorithm for classification, despite its name. (The name comes from the fact that logistic regression uses an extension of a linear regression model to model the probability of an input being in a particular class.) https://store.theartofservice.com/the-machine-learning-toolkit.html

86 Pattern recognition - Classification (machine learning)|Classification algorithms (supervised learning|supervised algorithms predicting categorical data|categorical labels) 1 Nonparametric:No distributional assumption regarding shape of feature distributions per class. https://store.theartofservice.com/the-machine-learning-toolkit.html

87 Pattern recognition - Classification (machine learning)|Classification algorithms (supervised learning|supervised algorithms predicting categorical data|categorical labels) 1 *Variable kernel density estimation#Use for statistical classification|Kernel estimation and K-nearest-neighbor algorithms https://store.theartofservice.com/the-machine-learning-toolkit.html

88 Pattern recognition - Classification (machine learning)|Classification algorithms (supervised learning|supervised algorithms predicting categorical data|categorical labels) 1 *Neural networks (multi-layer perceptrons) https://store.theartofservice.com/the-machine-learning-toolkit.html

89 Pattern recognition - Classification (machine learning)|Classification algorithms (supervised learning|supervised algorithms predicting categorical data|categorical labels) 1 *Support vector machines https://store.theartofservice.com/the-machine-learning-toolkit.html

90 List of machine learning algorithms - Supervised learning 1 * Artificial neural network https://store.theartofservice.com/the-machine-learning-toolkit.html

91 List of machine learning algorithms - Supervised learning 1 ** Spiking neural networks https://store.theartofservice.com/the-machine-learning-toolkit.html

92 List of machine learning algorithms - Supervised learning 1 * Inductive logic programming https://store.theartofservice.com/the-machine-learning-toolkit.html

93 List of machine learning algorithms - Supervised learning 1 * Gaussian process regression https://store.theartofservice.com/the-machine-learning-toolkit.html

94 List of machine learning algorithms - Supervised learning 1 * Group method of data handling (GMDH) https://store.theartofservice.com/the-machine-learning-toolkit.html

95 List of machine learning algorithms - Supervised learning 1 * Learning Automata https://store.theartofservice.com/the-machine-learning-toolkit.html

96 List of machine learning algorithms - Supervised learning 1 * Learning Vector Quantization https://store.theartofservice.com/the-machine-learning-toolkit.html

97 List of machine learning algorithms - Supervised learning 1 * Minimum message length (decision trees, decision graphs, etc.) https://store.theartofservice.com/the-machine-learning-toolkit.html

98 List of machine learning algorithms - Supervised learning 1 * Ripple down rules, a knowledge acquisition methodology https://store.theartofservice.com/the-machine-learning-toolkit.html

99 List of machine learning algorithms - Supervised learning 1 * Subsymbolic machine learning algorithms https://store.theartofservice.com/the-machine-learning-toolkit.html

100 List of machine learning algorithms - Supervised learning 1 * Support vector machines https://store.theartofservice.com/the-machine-learning-toolkit.html

101 List of machine learning algorithms - Supervised learning 1 * Information Fuzzy Networks|Information fuzzy networks (IFN) https://store.theartofservice.com/the-machine-learning-toolkit.html

102 List of machine learning algorithms - Statistical classification 1 ** Multinomial logistic regression https://store.theartofservice.com/the-machine-learning-toolkit.html

103 List of machine learning algorithms - Statistical classification 1 ** Support vector machines https://store.theartofservice.com/the-machine-learning-toolkit.html

104 List of machine learning algorithms - Unsupervised learning 1 * Radial basis function network https://store.theartofservice.com/the-machine-learning-toolkit.html

105 List of machine learning algorithms - Unsupervised learning 1 * Vector Quantization https://store.theartofservice.com/the-machine-learning-toolkit.html

106 List of machine learning algorithms - Association rule learning 1 * Association_rule_learning#FP- growth_algorithm|FP-growth algorithm https://store.theartofservice.com/the-machine-learning-toolkit.html

107 List of machine learning algorithms - Hierarchical clustering 1 * Conceptual clustering https://store.theartofservice.com/the-machine-learning-toolkit.html

108 List of machine learning algorithms - Deep learning 1 * Deep Convolutional neural networks https://store.theartofservice.com/the-machine-learning-toolkit.html

109 Identity resolution - Machine learning 1 Higher accuracy can often be achieved by using various other machine learning techniques, including a single-layer perceptron.Wilson, D https://store.theartofservice.com/the-machine-learning-toolkit.html

110 Bootstrapping - Artificial intelligence and machine learning 1 Bootstrapping is a technique used to iteratively improve a classifier (machine learning)|classifier's performance. Seed AI is a hypothesized type of strong Artificial Intelligence capable of recursion|recursive self-improvement. Having improved itself, it would become better at improving itself, potentially leading to an exponential increase in intelligence. No such AI is known to exist, but it remains an active field of research. https://store.theartofservice.com/the-machine-learning-toolkit.html

111 Bootstrapping - Artificial intelligence and machine learning 1 Seed AI is a significant part of some theories about the technological singularity: proponents believe that the development of seed AI will rapidly yield ever-smarter intelligence (via bootstrapping) and thus a new era. https://store.theartofservice.com/the-machine-learning-toolkit.html

112 Monte Carlo Machine Learning Library 1 The 'Monte Carlo Machine Learning Library (MCMLL)' is an open source C++ template library which already relies on some C++0x specs. MCMLL is licensed under the GNU GPL. It is developed under the 64 bit Linux OS. MCMLL should be usable on other platforms as well, since it is based on International Organization for Standardization|ISO C++. https://store.theartofservice.com/the-machine-learning-toolkit.html

113 Monte Carlo Machine Learning Library 1 The philosophy behind MCMLL is to have a broad range support for Monte Carlo methods to implement machine learning applications. Since Monte Carlo methods are inherently Parallel algorithm|parallelizable, the goal is to provide multi-threaded implementations of the most important methods. https://store.theartofservice.com/the-machine-learning-toolkit.html

114 Monte Carlo Machine Learning Library - Overview 1 * complete framework for vector and matrix computations https://store.theartofservice.com/the-machine-learning-toolkit.html

115 Monte Carlo Machine Learning Library - Overview 1 * multi-threaded support for generic Evolutionary algorithms (EA) https://store.theartofservice.com/the-machine-learning-toolkit.html

116 Monte Carlo Machine Learning Library - Overview 1 * support for generic Sequential Monte Carlo methods ('Particle Filtering'). https://store.theartofservice.com/the-machine-learning-toolkit.html

117 Monte Carlo Machine Learning Library - Overview 1 Example applications include: https://store.theartofservice.com/the-machine-learning-toolkit.html

118 Monte Carlo Machine Learning Library - Overview 1 * support for learning Artificial Neural Networks (ANN) using EA's https://store.theartofservice.com/the-machine-learning-toolkit.html

119 Monte Carlo Machine Learning Library - Overview 1 * example programs for Sequential Monte Carlo methods ('Particle Filtering') https://store.theartofservice.com/the-machine-learning-toolkit.html

120 Monte Carlo Machine Learning Library - Overview 1 * a benchmark suite for testing and implementing Evolutionary Algorithms. https://store.theartofservice.com/the-machine-learning-toolkit.html

121 Monte Carlo Machine Learning Library - Supported Evolutionary Algorithms 1 DOI= /TEVC without history, R2DE,Onay Urfalioglu and Orhan Arikan, Randomized and Rank Based Differential Evolution, Machine Learning and Applications, Fourth International Conference on, vol https://store.theartofservice.com/the-machine-learning-toolkit.html

122 Monte Carlo Machine Learning Library - Supported Evolutionary Algorithms 1 * Covariance Matrix Adaptation Evolution Strategies (CMA-ES) https://store.theartofservice.com/the-machine-learning-toolkit.html

123 Monte Carlo Machine Learning Library - Supported Sequential Monte Carlo Methods 1 For particle filtering, the Particle filter|Sequential Importance Resampling (SIR) method is supported. To create an SMC application based on MCMLL, one has to define an observation distribution, a transition distribution and optionally an importance distribution to be used in the SIR operator. https://store.theartofservice.com/the-machine-learning-toolkit.html

124 Online machine learning 1 Online machine learning is a model of inductive reasoning|induction that learns one instance at a time https://store.theartofservice.com/the-machine-learning-toolkit.html

125 Online machine learning 1 Third the algorithm receives the true label of the instance.Littlestone, Nick; (1988) Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm, Machine Learning (2), Kluwer Academic Publishers The third stage is the most crucial as the algorithm can use this label feedback to update its hypothesis for future trials https://store.theartofservice.com/the-machine-learning-toolkit.html

126 Online machine learning 1 Because on-line learning algorithms continually receive label feedback, the algorithms are able to adapt and learn in difficult situations https://store.theartofservice.com/the-machine-learning-toolkit.html

127 Online machine learning 1 Unfortunately, the main difficulty of on-line learning is also a result of the requirement for continual label feedback https://store.theartofservice.com/the-machine-learning-toolkit.html

128 Online machine learning - A prototypical online supervised learning algorithm 1 In the setting of supervised learning, or learning from examples, we are interested in learning a function f : X \to Y, where X is thought of as a space of inputs and Y as a space of outputs, that predicts well on instances that are drawn from a joint probability distribution p(x,y) on X \times Y https://store.theartofservice.com/the-machine-learning-toolkit.html

129 Online machine learning - A prototypical online supervised learning algorithm 1 In reality, the learner never knows the true distribution p(x,y) over instances https://store.theartofservice.com/the-machine-learning-toolkit.html

130 Online machine learning - A prototypical online supervised learning algorithm 1 The above paradigm is not well-suited to the online learning setting though, as it requires complete a priori knowledge of the entire training set https://store.theartofservice.com/the-machine-learning-toolkit.html

131 Online machine learning - The algorithm and its interpretations 1 Here we outline a prototypical online learning algorithm in the supervised learning setting and we discuss several interpretations of this algorithm https://store.theartofservice.com/the-machine-learning-toolkit.html

132 Online machine learning - The algorithm and its interpretations 1 where w_1 \gets 0, \nabla V(\langle w_t, x_t \rangle, y_t) is the gradient of the loss for the next data point (x_t, y_t) evaluated at the current linear functional w_t, and \gamma_t !-- Bot inserted parameter. Either remove it; or change its value to. for the cite to end in a., as necessary. --ref name=kushneryinreferences / https://store.theartofservice.com/the-machine-learning-toolkit.html

133 Weka (machine learning) 1 'Weka' (Waikato Environment for Knowledge Analysis) is a popular suite of machine learning software written in Java (programming language)|Java, developed at the University of Waikato, New Zealand. Weka is free software available under the GNU General Public License. https://store.theartofservice.com/the-machine-learning-toolkit.html

134 Weka (machine learning) - Description 1 The original non-Java version of Weka was a Tcl|TCL/TK front-end to (mostly third-party) modeling algorithms implemented in other programming languages, plus data preprocessing utilities in C (programming language)|C, and a Makefile-based system for running machine learning experiments https://store.theartofservice.com/the-machine-learning-toolkit.html

135 Weka (machine learning) - Description 1 * portability, since it is fully implemented in the Java programming language and thus runs on almost any modern computing platform https://store.theartofservice.com/the-machine-learning-toolkit.html

136 Weka (machine learning) - Description 1 * a comprehensive collection of data preprocessing and modeling techniques https://store.theartofservice.com/the-machine-learning-toolkit.html

137 Weka (machine learning) - Description 1 * ease of use due to its graphical user interfaces https://store.theartofservice.com/the-machine-learning-toolkit.html

138 Weka (machine learning) - Description 1 Weka supports several standard data mining tasks, more specifically, data preprocessing, data clustering|clustering, Statistical classification|classification, Regression analysis|regression, visualization, and feature selection https://store.theartofservice.com/the-machine-learning-toolkit.html

139 Weka (machine learning) - Description 1 Weka's main user interface is the Explorer, but essentially the same functionality can be accessed through the component- based Knowledge Flow interface and from the command line. There is also the Experimenter, which allows the systematic comparison of the predictive performance of Weka's machine learning algorithms on a collection of datasets. https://store.theartofservice.com/the-machine-learning-toolkit.html

140 Weka (machine learning) - Description 1 The Explorer interface features several panels providing access to the main components of the workbench: https://store.theartofservice.com/the-machine-learning-toolkit.html

141 Weka (machine learning) - Description 1 * The Preprocess panel has facilities for importing data from a database, a Comma-separated values|CSV file, etc., and for preprocessing this data using a so- called filtering algorithm. These filters can be used to transform the data (e.g., turning numeric attributes into discrete ones) and make it possible to delete instances and attributes according to specific criteria. https://store.theartofservice.com/the-machine-learning-toolkit.html

142 Weka (machine learning) - Description 1 * The Classify panel enables the user to apply Statistical classification|classification and Regression analysis|regression algorithms (indiscriminately called classifiers in Weka) to the resulting dataset, to estimate the accuracy of the resulting Predictive modeling|predictive model, and to visualize erroneous predictions, Receiver operating characteristic|ROC curves, etc., or the model itself (if the model is amenable to visualization like, e.g., a decision tree). https://store.theartofservice.com/the-machine-learning-toolkit.html

143 Weka (machine learning) - Description 1 * The Associate panel provides access to Association rule learning|association rule learners that attempt to identify all important interrelationships between attributes in the data. https://store.theartofservice.com/the-machine-learning-toolkit.html

144 Weka (machine learning) - Description 1 * The Cluster panel gives access to the cluster analysis|clustering techniques in Weka, e.g., the simple k-means algorithm. There is also an implementation of the Expectation-maximization algorithm|expectation maximization algorithm for learning a mixture of normal distributions. https://store.theartofservice.com/the-machine-learning-toolkit.html

145 Weka (machine learning) - Description 1 * The Select attributes panel provides algorithms for identifying the most predictive attributes in a dataset. https://store.theartofservice.com/the-machine-learning-toolkit.html

146 Weka (machine learning) - Description 1 * The Visualize panel shows a scatter plot matrix, where individual scatter plots can be selected and enlarged, and analyzed further using various selection operators. https://store.theartofservice.com/the-machine-learning-toolkit.html

147 Weka (machine learning) - History 1 * In 1993, the University of Waikato in New Zealand started development of the original version of Weka (which became a mixture of TCL/TK, C, and Makefiles). https://store.theartofservice.com/the-machine-learning-toolkit.html

148 Weka (machine learning) - History 1 * In 1997, the decision was made to redevelop Weka from scratch in Java, including implementations of modeling algorithms. https://store.theartofservice.com/the-machine-learning-toolkit.html

149 Weka (machine learning) - History 1 * In 2006, Pentaho|Pentaho Corporation acquired an exclusive licence to use Weka for business intelligence. It forms the data mining and predictive analytics component of the Pentaho business intelligence suite. https://store.theartofservice.com/the-machine-learning-toolkit.html

150 Weka (machine learning) - History 1 * [ pe=downloadsoffset=200 All-time ranking] on Sourceforge.net as of , 243 (with 2,487,213 downloads) https://store.theartofservice.com/the-machine-learning-toolkit.html

151 Machine Learning (journal) 1 'Machine Learning' is a peer-review|peer-reviewed scientific journal, published since https://store.theartofservice.com/the-machine-learning-toolkit.html

152 Machine Learning (journal) 1 In 2001, forty editors and members of the editorial board of Machine Learning resigned in order to found the Journal of Machine Learning Research (JMLR), saying that in the era of the internet, it was detrimental for researchers to continue publishing their papers in expensive journals with pay-access archives. Instead, they wrote, they supported the model of JMLR, in which authors retained copyright over their papers and archives were freely available on the internet. https://store.theartofservice.com/the-machine-learning-toolkit.html

153 Journal of Machine Learning Research 1 The 'Journal of Machine Learning Research' (usually abbreviated 'JMLR'), is a scientific journal focusing on machine learning, a subfield of Artificial Intelligence. It was founded in https://store.theartofservice.com/the-machine-learning-toolkit.html

154 Journal of Machine Learning Research 1 In 2001, forty editors of Machine Learning resigned in order to support JMLR, saying that in the era of the internet, it was detrimental for researchers to continue publishing their papers in expensive journals with pay-access archives https://store.theartofservice.com/the-machine-learning-toolkit.html

155 Journal of Machine Learning Research 1 Print editions of JMLR were published by MIT Press until 2004, and by Microtome Publishing thereafter. https://store.theartofservice.com/the-machine-learning-toolkit.html

156 Journal of Machine Learning Research 1 Since Summer 2007 JMLR is also publishing [http://www.jmlr.org/mloss Machine Learning Open Source Software ]. https://store.theartofservice.com/the-machine-learning-toolkit.html

157 Boosting (machine learning) 1 Boosting is based on the question posed by Kearns:Michael Kearns (1988); [http://www.cis.upenn.edu/~mkearns/papers/ boostnote.pdf Thoughts on Hypothesis Boosting], Unpublished manuscript (Machine Learning class project, December 1988) Can a set of 'weak learners' create a single 'strong learner'? A weak learner is defined to be a classifier which is only slightly correlated with the true classification (it can label examples better than random guessing) https://store.theartofservice.com/the-machine-learning-toolkit.html

158 Boosting (machine learning) 1 Schapire's affirmative answer to Kearns' question has had significant ramifications in machine learning and statistics, most notably leading to the development of boosting. https://store.theartofservice.com/the-machine-learning-toolkit.html

159 Boosting (machine learning) 1 When first introduced, the hypothesis boosting problem simply referred to the process of turning a weak learner into a strong learner https://store.theartofservice.com/the-machine-learning-toolkit.html

160 Boosting (machine learning) - Boosting algorithms 1 While boosting is not algorithmically constrained, most boosting algorithms consist of iteratively learning weak classifiers with respect to a distribution and adding them to a final strong classifier https://store.theartofservice.com/the-machine-learning-toolkit.html

161 Boosting (machine learning) - Boosting algorithms 1 There are many boosting algorithms. The original ones, proposed by Robert Schapire (a recursive majority gate formulation) and Yoav Freund (boost by majorityLlew Mason, Jonathan Baxter, Peter Bartlett, and Marcus Frean (2000); Boosting Algorithms as Gradient Descent, in S https://store.theartofservice.com/the-machine-learning-toolkit.html

162 Boosting (machine learning) - Examples of boosting algorithms 1 The main variation between many boosting algorithms is their method of weighting training data points and hypotheses https://store.theartofservice.com/the-machine-learning-toolkit.html

163 Boosting (machine learning) - Criticism 1 In 2008 Phillip Long (at Google) and Rocco A. Servedio (Columbia University) published [http://www.phillong.info/publications/LS10 _potential.pdf a paper] at the 25th International Conference for Machine Learning suggesting that many of these algorithms are probably flawed. They conclude that convex potential boosters cannot withstand random classification https://store.theartofservice.com/the-machine-learning-toolkit.html

164 Boosting (machine learning) - Criticism 1 Servedio (2010); Random Classification Noise Defeats All Convex Potential Boosters, Machine Learning 78(3), pp https://store.theartofservice.com/the-machine-learning-toolkit.html

165 Transduction (machine learning) 1 In logic, statistical inference, and supervised learning, https://store.theartofservice.com/the-machine-learning-toolkit.html

166 Transduction (machine learning) 1 'transduction' or 'transductive inference' is reasoning from https://store.theartofservice.com/the-machine-learning-toolkit.html

167 Transduction (machine learning) 1 induction (philosophy)|induction is reasoning from observed training cases https://store.theartofservice.com/the-machine-learning-toolkit.html

168 Transduction (machine learning) 1 to general rules, which are then applied to the test cases. The distinction is https://store.theartofservice.com/the-machine-learning-toolkit.html

169 Transduction (machine learning) 1 most interesting in cases where the predictions of the transductive model are https://store.theartofservice.com/the-machine-learning-toolkit.html

170 Transduction (machine learning) 1 not achievable by any inductive model. Note that this is caused by transductive https://store.theartofservice.com/the-machine-learning-toolkit.html

171 Transduction (machine learning) 1 inference on different test sets producing mutually inconsistent predictions. https://store.theartofservice.com/the-machine-learning-toolkit.html

172 Transduction (machine learning) 1 Transduction was introduced by Vladimir Vapnik in the 1990s, motivated by https://store.theartofservice.com/the-machine-learning-toolkit.html

173 Transduction (machine learning) 1 his view that transduction is preferable to induction since, according to him, induction requires https://store.theartofservice.com/the-machine-learning-toolkit.html

174 Transduction (machine learning) 1 solving a more general problem (inferring a function) before solving a more https://store.theartofservice.com/the-machine-learning-toolkit.html

175 Transduction (machine learning) 1 specific problem (computing outputs for new cases): When solving a problem of https://store.theartofservice.com/the-machine-learning-toolkit.html

176 Transduction (machine learning) 1 An example of learning which is not inductive would be in the case of binary https://store.theartofservice.com/the-machine-learning-toolkit.html

177 Transduction (machine learning) 1 classification, where the inputs tend to cluster in two groups. A large set of https://store.theartofservice.com/the-machine-learning-toolkit.html

178 Transduction (machine learning) 1 test inputs may help in finding the clusters, thus providing useful information https://store.theartofservice.com/the-machine-learning-toolkit.html

179 Transduction (machine learning) 1 about the classification labels. The same predictions would not be obtainable https://store.theartofservice.com/the-machine-learning-toolkit.html

180 Transduction (machine learning) 1 from a model which induces a function based only on the training cases. Some https://store.theartofservice.com/the-machine-learning-toolkit.html

181 Transduction (machine learning) 1 people may call this an example of the closely related semi-supervised learning, since Vapnik's motivation is quite different. An example of an algorithm in this category is the Transductive Support Vector Machine (TSVM). https://store.theartofservice.com/the-machine-learning-toolkit.html

182 Transduction (machine learning) 1 A third possible motivation which leads to transduction arises through the need https://store.theartofservice.com/the-machine-learning-toolkit.html

183 Transduction (machine learning) 1 to approximate. If exact inference is computationally prohibitive, one may at https://store.theartofservice.com/the-machine-learning-toolkit.html

184 Transduction (machine learning) 1 least try to make sure that the approximations are good at the test inputs. In https://store.theartofservice.com/the-machine-learning-toolkit.html

185 Transduction (machine learning) 1 this case, the test inputs could come from an arbitrary distribution (not https://store.theartofservice.com/the-machine-learning-toolkit.html

186 Transduction (machine learning) 1 necessarily related to the distribution of the training inputs), which wouldn't https://store.theartofservice.com/the-machine-learning-toolkit.html

187 Transduction (machine learning) 1 be allowed in semi-supervised learning. An example of an algorithm falling in https://store.theartofservice.com/the-machine-learning-toolkit.html

188 Transduction (machine learning) - Example Problem 1 The following example problem contrasts some of the unique properties of transduction against induction. https://store.theartofservice.com/the-machine-learning-toolkit.html

189 Transduction (machine learning) - Example Problem 1 A collection of points is given, such that some of the points are labeled (A, B, or C), but most of the points are unlabeled (?). The goal is to predict appropriate labels for all of the unlabeled points. https://store.theartofservice.com/the-machine-learning-toolkit.html

190 Transduction (machine learning) - Example Problem 1 The inductive approach to solving this problem is to use the labeled points to train a supervised learning algorithm, and then have it predict labels for all of the unlabeled points https://store.theartofservice.com/the-machine-learning-toolkit.html

191 Transduction (machine learning) - Example Problem 1 Transduction has the advantage of being able to consider all of the points, not just the labeled points, while performing the labeling task. In this case, transductive algorithms would label the unlabeled points according to the clusters to which they naturally belong. The points in the middle, therefore, would most likely be labeled B, because they are packed very close to that cluster. https://store.theartofservice.com/the-machine-learning-toolkit.html

192 Transduction (machine learning) - Example Problem 1 An advantage of transduction is that it may be able to make better predictions with fewer labeled points, because it uses the natural breaks found in the unlabeled points https://store.theartofservice.com/the-machine-learning-toolkit.html

193 Transduction (machine learning) - Transduction Algorithms 1 Transduction algorithms can be broadly divided into two categories: those that seek to assign discrete labels to unlabeled points, and those that seek to regress continuous labels for unlabeled points https://store.theartofservice.com/the-machine-learning-toolkit.html

194 Transduction (machine learning) - Partitioning Transduction 1 Partitioning transduction can be thought of as top-down transduction. It is a semi- supervised extension of partition-based clustering. It is typically performed as follows: https://store.theartofservice.com/the-machine-learning-toolkit.html

195 Transduction (machine learning) - Partitioning Transduction 1 Of course, any reasonable partitioning technique could be used with this algorithm. Max flow min cut partitioning schemes are very popular for this purpose. https://store.theartofservice.com/the-machine-learning-toolkit.html

196 Transduction (machine learning) - Agglomerative Transduction 1 Agglomerative transduction can be thought of as bottom-up transduction. It is a semi-supervised extension of agglomerative clustering. It is typically performed as follows: https://store.theartofservice.com/the-machine-learning-toolkit.html

197 Transduction (machine learning) - Agglomerative Transduction 1 Compute the pair-wise distances, D, between all the points. https://store.theartofservice.com/the-machine-learning-toolkit.html

198 Transduction (machine learning) - Agglomerative Transduction 1 Consider each point to be a cluster of size 1. https://store.theartofservice.com/the-machine-learning-toolkit.html

199 Transduction (machine learning) - Agglomerative Transduction 1 If (a is unlabeled) or (b is unlabeled) or (a and b have the same label) https://store.theartofservice.com/the-machine-learning-toolkit.html

200 Transduction (machine learning) - Agglomerative Transduction 1 Merge the two clusters that contain a and b. https://store.theartofservice.com/the-machine-learning-toolkit.html

201 Transduction (machine learning) - Agglomerative Transduction 1 Label all points in the merged cluster with the same label. https://store.theartofservice.com/the-machine-learning-toolkit.html

202 Transduction (machine learning) - Manifold Transduction 1 Manifold-learning-based transduction is still a very young field of research. https://store.theartofservice.com/the-machine-learning-toolkit.html

203 BodyMedia - Wearable device and machine learning expertise 1 The BodyMedia informatics group made available a large anonymised human physiology data set for the 2004 International Conference on Machine Learning, running a Machine Learning Challenge https://store.theartofservice.com/the-machine-learning-toolkit.html

204 Learning curve - In machine learning 1 The machine learning curve is useful for many purposes including comparing different algorithms, choosing model parameters during design, adjusting optimization to improve convergence, and determining the amount of data used for training. https://store.theartofservice.com/the-machine-learning-toolkit.html

205 Protein structure prediction - Machine learning 1 Artificial neural network|Neural network methods use training sets of solved structures to identify common sequence motifs associated with particular arrangements of secondary structures https://store.theartofservice.com/the-machine-learning-toolkit.html

206 Protein structure prediction - Machine learning 1 Support vector machines have proven particularly useful for predicting the locations of turn (biochemistry)|turns, which are difficult to identify with statistical methods. The requirement of relatively small training sets has also been cited as an advantage to avoid overfitting to existing structural data. https://store.theartofservice.com/the-machine-learning-toolkit.html

207 Protein structure prediction - Machine learning 1 Extensions of machine learning techniques attempt to predict more fine- grained local properties of proteins, such as protein backbone|backbone dihedral angles in unassigned regions. Both SVMs and neural networks have been applied to this problem. More recently, real-value torsion angles can be accurately predicted by SPINE-X and successfully employed for ab initio structure prediction. https://store.theartofservice.com/the-machine-learning-toolkit.html

208 Predictive Analysis - Machine learning techniques 1 For such cases, machine learning techniques emulate human cognition and learn from training examples to predict future events. https://store.theartofservice.com/the-machine-learning-toolkit.html

209 Artificial intelligence marketing - Machine Learning 1 Machine learning is concerned with the design and development of algorithms and techniques that allow computers to learn. https://store.theartofservice.com/the-machine-learning-toolkit.html

210 Artificial intelligence marketing - Machine Learning 1 As defined above machine learning is one of the techniques that can be employed to enable more effective 'behavioral targeting' https://store.theartofservice.com/the-machine-learning-toolkit.html

211 Bootstrap - Artificial intelligence and machine learning 1 Bootstrapping is a technique used to iteratively improve a classifier (machine learning)|classifier's performance. Seed AI is a hypothesized type of artificial intelligence capable of recursive self- improvement. Having improved itself, it would become better at improving itself, potentially leading to an exponential increase in intelligence. No such AI is known to exist, but it remains an active field of research. https://store.theartofservice.com/the-machine-learning-toolkit.html

212 Academic studies about Wikipedia - Machine learning 1 Automated Semantic data model|semantic knowledge extraction using machine learning algorithms is used to extract machine-processable information at a relatively low complexity cost. DBpedia uses structured content extracted from infoboxes by machine learning algorithms to create a resource of linked data in a Semantic Web. https://store.theartofservice.com/the-machine-learning-toolkit.html

213 Concept learning - Machine learning approaches to concept learning 1 In machine learning, algorithms of exemplar theory are also known as instance learners or lazy learners. https://store.theartofservice.com/the-machine-learning-toolkit.html

214 Concept learning - Machine learning approaches to concept learning 1 #Data Mining: using historical data to improve decisions. An example is looking at medical records and then applying one's medical knowledge to make a diagnosis. https://store.theartofservice.com/the-machine-learning-toolkit.html

215 Concept learning - Machine learning approaches to concept learning 1 #Software applications that cannot be programmed by hand: examples are autonomous driving and speech recognition https://store.theartofservice.com/the-machine-learning-toolkit.html

216 Concept learning - Machine learning approaches to concept learning 1 #Self-customizing programs: an example is a newsreader that learns a reader's particular interests and highlights them when the reader visits the site. https://store.theartofservice.com/the-machine-learning-toolkit.html

217 Concept learning - Machine learning approaches to concept learning 1 Machine learning has an exciting future. Some potential advantages include: learning across full mixed-media data, learning across multiple internal databases (including the Internet and news feeds), learning by active experimentation, learning decisions rather than predictions, and the possibility of programming languages with embedded learning. https://store.theartofservice.com/the-machine-learning-toolkit.html

218 List of algorithms - Machine learning and statistical classification 1 * Association rule learning: discover interesting relations between variables, used in data mining https://store.theartofservice.com/the-machine-learning-toolkit.html

219 List of algorithms - Machine learning and statistical classification 1 ** Association rule learning#Eclat algorithm|Eclat algorithm https://store.theartofservice.com/the-machine-learning-toolkit.html

220 List of algorithms - Machine learning and statistical classification 1 ** Association rule learning#FP-growth algorithm|FP-growth algorithm https://store.theartofservice.com/the-machine-learning-toolkit.html

221 List of algorithms - Machine learning and statistical classification 1 ** One-attribute rule https://store.theartofservice.com/the-machine-learning-toolkit.html

222 List of algorithms - Machine learning and statistical classification 1 ** Association rule learning#Zero-attribute rule|Zero-attribute rule https://store.theartofservice.com/the-machine-learning-toolkit.html

223 List of algorithms - Machine learning and statistical classification 1 * Boosting (meta- algorithm): Use many weak learners to boost effectiveness https://store.theartofservice.com/the-machine-learning-toolkit.html

224 List of algorithms - Machine learning and statistical classification 1 ** BrownBoost:a boosting algorithm that may be robust to noisy datasets https://store.theartofservice.com/the-machine-learning-toolkit.html

225 List of algorithms - Machine learning and statistical classification 1 * Bootstrap aggregating (bagging): technique to improve stability and classification accuracy https://store.theartofservice.com/the-machine-learning-toolkit.html

226 List of algorithms - Machine learning and statistical classification 1 ** ID3 algorithm (Iterative Dichotomiser 3): Use heuristic to generate small decision trees https://store.theartofservice.com/the-machine-learning-toolkit.html

227 List of algorithms - Machine learning and statistical classification 1 * k-nearest neighbors (k-NN): a method for classifying objects based on closest training examples in the feature space https://store.theartofservice.com/the-machine-learning-toolkit.html

228 List of algorithms - Machine learning and statistical classification 1 * Linde–Buzo–Gray algorithm: a vector quantization algorithm used to derive a good codebook https://store.theartofservice.com/the-machine-learning-toolkit.html

229 List of algorithms - Machine learning and statistical classification 1 * Locality-sensitive hashing (LSH): a method of performing probabilistic dimension reduction of high-dimensional data https://store.theartofservice.com/the-machine-learning-toolkit.html

230 List of algorithms - Machine learning and statistical classification 1 ** Backpropagation: A supervised learning method which requires a teacher that knows, or can calculate, the desired output for any given input https://store.theartofservice.com/the-machine-learning-toolkit.html

231 List of algorithms - Machine learning and statistical classification 1 ** Hopfield net: a Recurrent neural network in which all connections are symmetric https://store.theartofservice.com/the-machine-learning-toolkit.html

232 List of algorithms - Machine learning and statistical classification 1 ** Perceptron: the simplest kind of feedforward neural network: a linear classifier. https://store.theartofservice.com/the-machine-learning-toolkit.html

233 List of algorithms - Machine learning and statistical classification 1 ** Pulse-coupled neural networks (PCNN): Neural network|neural models proposed by modeling a cat's visual cortex and developed for high-performance Bionics|biomimetic image processing. https://store.theartofservice.com/the-machine-learning-toolkit.html

234 List of algorithms - Machine learning and statistical classification 1 ** Radial basis function network: an artificial neural network that uses radial basis functions as activation functions https://store.theartofservice.com/the-machine-learning-toolkit.html

235 List of algorithms - Machine learning and statistical classification 1 ** Self-organizing map: an unsupervised network that produces a low-dimensional representation of the input space of the training samples https://store.theartofservice.com/the-machine-learning-toolkit.html

236 List of algorithms - Machine learning and statistical classification 1 * Random forest: classify using many decision trees https://store.theartofservice.com/the-machine-learning-toolkit.html

237 List of algorithms - Machine learning and statistical classification 1 ** Q-learning: learn an action-value function that gives the expected utility of taking a given action in a given state and following a fixed policy thereafter https://store.theartofservice.com/the-machine-learning-toolkit.html

238 List of algorithms - Machine learning and statistical classification 1 * Relevance Vector Machine (RVM): similar to SVM, but provides probabilistic classification https://store.theartofservice.com/the-machine-learning-toolkit.html

239 List of algorithms - Machine learning and statistical classification 1 * Support Vector Machines (SVM): a set of methods which divide multidimensional data by finding a dividing hyperplane with the maximum margin between the two sets https://store.theartofservice.com/the-machine-learning-toolkit.html

240 List of algorithms - Machine learning and statistical classification 1 ** Structured SVM: allows training of a classifier for general structured output labels. https://store.theartofservice.com/the-machine-learning-toolkit.html

241 List of algorithms - Machine learning and statistical classification 1 * Winnow algorithm: related to the perceptron, but uses a multiplicative weight-update scheme https://store.theartofservice.com/the-machine-learning-toolkit.html

242 Torch (machine learning) 1 'Torch' is an open source deep learning library for the Lua (programming language)|Lua programming language https://store.theartofservice.com/the-machine-learning-toolkit.html

243 Torch (machine learning) 1 and a scientific computing framework with wide support for machine learning algorithms. It uses a fast scripting language LuaJIT, and an underlying C (programming language)|C implementation. https://store.theartofservice.com/the-machine-learning-toolkit.html

244 Torch (machine learning) - torch 1 The core package of Torch is [https://github.com/torch/torch7 torch] https://store.theartofservice.com/the-machine-learning-toolkit.html

245 Torch (machine learning) - torch 1 The following exemplifies using torch via its REPL interpreter: https://store.theartofservice.com/the-machine-learning-toolkit.html

246 Torch (machine learning) - torch 1 It also has StochasticGradient class for training a neural network using Stochastic gradient descent, although the Optim package provides much more options in this respect, like momentum and weight decay Regularization (mathematics)|regularization. https://store.theartofservice.com/the-machine-learning-toolkit.html

247 Torch (machine learning) - Other packages 1 Many packages other than the above official packages are used with Torch. These are listed in the [https://github.com/torch/torch7/wiki/Cheat sheet torch cheatsheet]. These extra packages provide a wide range of utilities such as parallelism, asynchronous input/output, image processing, and so on. https://store.theartofservice.com/the-machine-learning-toolkit.html

248 Torch (machine learning) - Applications 1 Torch is used by DeepMind Technologies|Google DeepMind,[http://blog.mikiobraun.de/2014/ 01/what-deepmind-google.html What is going on with DeepMind and Google?] https://store.theartofservice.com/the-machine-learning-toolkit.html

249 Torch (machine learning) - Applications 1 the Facebook AI Research Group,[http://www.kdnuggets.com/2014/02/exclusive-yann- lecun-deep-learning-facebook-ai-lab.html KDnuggets Interview with Yann LeCun, Deep Learning Expert, Director of Facebook AI Lab] the Computational Intelligence, Learning, Vision, and Robotics Lab at NYU,[http://cilvr.nyu.edu/doku.php?id=code:start CILVR Lab Software] MADBITS,[http://code.madbits.com/wiki/doku.php Machine Learning with Torch7] IBM,[https://news.ycombinator.com/item?id= Hacker News] Yandex[https://www.facebook.com/yann.lecun/posts/ ?comment_id= offset=0total_comments=6 Yann Lecun's FaceBook Page] and the Idiap Research Institute.[https://www.idiap.ch/scientific-research/resources/torch IDIAP Research Institute : Torch] It is used and cited in 240 research papers.[http://scholar.google.ca/scholar?cites= as_sdt=2005sciodt= 0,5hl=en Google Scholar results for Torch: a modular machine learning software library citations] For comparison, Theano (software)|Theano, a similar library written in Python (programming language), C and CUDA, has 138 citations.[http://scholar.google.ca/scholar?cites= as_sdt=2005sciodt =0,5hl=en Theano: a CPU and GPU math expression compiler] Torch has been extended for use on Android (operating system)|Android[https://github.com/soumith/torch-android Torch-android GitHub repository] and iOS.[https://github.com/clementfarabet/torch-ios Torch-ios GitHub repository] It has been used to build hardware implementations for data flows like those found in neural networks.[http://pub.clement.farabet.net/ecvw11.pdf NeuFlow: A Runtime Reconfigurable Dataflow Processor for Vision] https://store.theartofservice.com/the-machine-learning-toolkit.html

250 Overfitting - Machine learning 1 The concept of overfitting is important in machine learning https://store.theartofservice.com/the-machine-learning-toolkit.html

251 Overfitting - Machine learning 1 As a simple example, consider a database of retail purchases that includes the item bought, the purchaser, and the date and time of purchase. It's easy to construct a model that will fit the training set perfectly by using the date and time of purchase to predict the other attributes; but this model will not generalize at all to new data, because those past times will never occur again. https://store.theartofservice.com/the-machine-learning-toolkit.html

252 Overfitting - Machine learning 1 Generally, a learning algorithm is said to overfit relative to a simpler one if it is more accurate in fitting known data (hindsight) but less accurate in predicting new data (foresight) https://store.theartofservice.com/the-machine-learning-toolkit.html

253 Training set - Use in artificial intelligence, machine learning, and statistics 1 In artificial intelligence or machine learning, a training set consists of an input Array data structure|vector and an answer vector, and is used together with a supervised learning method to train a knowledge database (e.g. a neural net or a naive bayes classifier) used by an AI machine. https://store.theartofservice.com/the-machine-learning-toolkit.html

254 Training set - Use in artificial intelligence, machine learning, and statistics 1 In statistics|statistical modeling, a training set is used to fit a model that can be used to predict a response value from one or more predictors. The fitting can include both feature selection|variable selection and parameter estimation theory|estimation. Statistical models used for prediction are often called regression analysis|regression models, of which linear regression and logistic regression are two examples. https://store.theartofservice.com/the-machine-learning-toolkit.html

255 Training set - Use in artificial intelligence, machine learning, and statistics 1 In these fields, a major emphasis is placed on avoiding overfitting, so as to achieve the best possible performance on an independent test set that follows the same probability distribution as the training set. https://store.theartofservice.com/the-machine-learning-toolkit.html

256 Tanagra (machine learning) 1 'Tanagra' is a free suite of machine learning software for research and academic purposes https://store.theartofservice.com/the-machine-learning-toolkit.html

257 Tanagra (machine learning) 1 developed by Ricco Rakotomalala at the Lumière University Lyon 2, France. https://store.theartofservice.com/the-machine-learning-toolkit.html

258 Tanagra (machine learning) 1 Tanagra supports several standard data mining tasks such as: Visualization, Descriptive statistics, Instance selection, feature selection, feature construction, regression analysis|regression, factor analysis, data clustering|clustering, statistical classification|classification and association rule learning. https://store.theartofservice.com/the-machine-learning-toolkit.html

259 Tanagra (machine learning) 1 Tanagra is an academic project https://store.theartofservice.com/the-machine-learning-toolkit.html

260 Tanagra (machine learning) - History 1 The development of Tanagra was started in June The first version is distributed in December Tanagra is the successor of Sipina, another free data mining tool which is intended only for the supervised learning tasks (classification), especially an interactive and visual construction of decision trees. Sipina is still available online and is maintained. https://store.theartofservice.com/the-machine-learning-toolkit.html

261 Tanagra (machine learning) - History 1 Tanagra is an open source project as every researcher can access to the source code, and add his own algorithms, as far as he agrees and conforms to the software distribution license. https://store.theartofservice.com/the-machine-learning-toolkit.html

262 Tanagra (machine learning) - History 1 The main purpose of Tanagra project is to give researchers and students a user- friendly data mining software, conforming to the present norms of the software development in this domain (especially in the design of its GUI and the way to use it), and allowing to analyze either real or synthetic data. https://store.theartofservice.com/the-machine-learning-toolkit.html

263 Tanagra (machine learning) - History 1 From 2006, Ricco Rakotomalala made an important documentation effort. A large number of tutorials are published on a dedicated website. They describe the statistical and machine learning methods and their implementation with Tanagra on real case studies. The use of the other free data mining tools on the same problems is also widely described. The comparison of the tools enables to the readers to understand the possible differences in the presenting of results. https://store.theartofservice.com/the-machine-learning-toolkit.html

264 Tanagra (machine learning) - Description 1 Each node is a statistical or machine learning technique, the connection between two nodes represents the data transfer https://store.theartofservice.com/the-machine-learning-toolkit.html

265 Tanagra (machine learning) - Description 1 Tanagra makes a good compromise between the statistical approaches (e.g. parametric and nonparametric statistical tests), the multivariate analysis methods (e.g. factor analysis, correspondence analysis, cluster analysis, regression) and the machine learning techniques (e.g. neural network, support vector machine, decision trees, random forest). https://store.theartofservice.com/the-machine-learning-toolkit.html

266 Music Information Retrieval - Statistics and Machine Learning 1 *Computational methods for classification, clustering, and modelling — musical feature extraction for mono- and polyphonic music, similarity and pattern matching, retrieval https://store.theartofservice.com/the-machine-learning-toolkit.html

267 Music Information Retrieval - Statistics and Machine Learning 1 * Formal methods and databases — applications of automated music identification and recognition, such as score following, automatic accompaniment, routing and filtering for music and music queries, query languages, standards and other metadata or protocols for music information handling and information retrieval|retrieval, multi- agent systems, distributed search) https://store.theartofservice.com/the-machine-learning-toolkit.html

268 Music Information Retrieval - Statistics and Machine Learning 1 *Software for music information retrieval — Semantic Web and musical digital objects, intelligent agents, collaborative software, web-based search and semantic retrieval, query by humming, acoustic fingerprinting https://store.theartofservice.com/the-machine-learning-toolkit.html

269 Music Information Retrieval - Statistics and Machine Learning 1 * Music analysis and knowledge representation — automatic summarization, citing, excerpting, downgrading, transformation, formal models of music, digital scores and representations, music indexing and metadata. https://store.theartofservice.com/the-machine-learning-toolkit.html

270 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 1 'ECML PKDD', the 'European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases', is one of the leading ECML is number 4 on the list. Both ECML and PKDD are ranked on “tier A”. academic conferences on machine learning and knowledge discovery, held in Europe every year. https://store.theartofservice.com/the-machine-learning-toolkit.html

271 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - History 1 ECML PKDD is a merger of two European conferences, 'European Conference on Machine Learning' ('ECML') and 'European Conference on Principles and Practice of Knowledge Discovery in Databases' ('PKDD'). ECML and PKDD have been co- located since 2001; however, both ECML and PKDD retained their own identity until For example, the 2007 conference was known as “the 18th European Conference on Machine Learning (ECML) and the 11th European Conference https://store.theartofservice.com/the-machine-learning-toolkit.html

272 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - History 1 The history of ECML dates back to 1986, when the European Working Session on Learning was first held. In 1993 the name of the conference was changed to European Conference on Machine Learning. https://store.theartofservice.com/the-machine-learning-toolkit.html

273 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - History 1 PKDD was first organised in Originally PKDD stood for the European Symposium on Principles of Data Mining and Knowledge Discovery from Databases.. The name European Conference on Principles and Practice of Knowledge Discovery in Databases was used since https://store.theartofservice.com/the-machine-learning-toolkit.html

274 Feature (machine learning) 1 In machine learning and pattern recognition, a 'feature' is an individual measurable heuristic property of a phenomenon being observed. Choosing discriminating and independent features is key to any pattern recognition algorithm being successful in classification (machine learning)|classification. Features are usually numeric, but structural features such as string (computer science)|strings and graph (mathematics)|graphs are used in syntactic pattern recognition. https://store.theartofservice.com/the-machine-learning-toolkit.html

275 Feature (machine learning) 1 The set of features of a given data instance is often grouped into a feature vector. The reason for doing this is that the vector can be treated mathematically. For example, many algorithms compute a score for classifying an instance into a particular category by linearly combining a feature vector with a vector of weights, using a linear predictor function. https://store.theartofservice.com/the-machine-learning-toolkit.html

276 Feature (machine learning) 1 The concept of feature is essentially the same as the concept of explanatory variable used in statistics|statistical techniques such as linear regression. https://store.theartofservice.com/the-machine-learning-toolkit.html

277 Feature (machine learning) - Classification 1 While different areas of pattern recognition obviously have different features, once the features are decided, they are classified by a much smaller set of algorithms. These include k-nearest neighbor algorithm|nearest neighbor classification in multiple dimensions, neural networks or statistical classification|statistical techniques such as Bayesian inference|Bayesian approaches. https://store.theartofservice.com/the-machine-learning-toolkit.html

278 Feature (machine learning) - Examples 1 In character recognition, features may include horizontal and vertical profiles, number of internal holes, stroke detection and many others. https://store.theartofservice.com/the-machine-learning-toolkit.html

279 Feature (machine learning) - Examples 1 In speech recognition, features for recognizing phonemes can include noise ratios, length of sounds, relative power, filter matches and many others. https://store.theartofservice.com/the-machine-learning-toolkit.html

280 Feature (machine learning) - Examples 1 In spam (electronic)|spam detection algorithms, features may include whether certain headers are present or absent, whether they are well formed, what language the appears to be, the grammatical correctness of the text, Markovian frequency analysis and many others. https://store.theartofservice.com/the-machine-learning-toolkit.html

281 Feature (machine learning) - Examples 1 In all these cases, and many others, feature extraction|extracting features that are measurable by a computer is an art, and with the exception of some neural networking and genetic techniques that automatically intuit features, hand selection of good features forms the basis of almost all classification algorithms. https://store.theartofservice.com/the-machine-learning-toolkit.html

282 Regularization (mathematics) - Regularization in statistics and machine learning 1 The most common variants in machine learning are and regularization, which can be added to learning algorithms that minimize a loss function by instead minimizing, where is the model's weight vector, ‖ · ‖ is either the norm or the squared norm, and α is a free parameter that needs to be tuned empirically (typically by Cross-validation (statistics)|cross-validation; see hyperparameter optimization) https://store.theartofservice.com/the-machine-learning-toolkit.html

283 Regularization (mathematics) - Regularization in statistics and machine learning 1 regularization is often preferred because it produces sparse models and thus performs feature selection within the learning algorithm, but since the norm is not differentiable, it may require changes to learning algorithms, in particular gradient-based learners. https://store.theartofservice.com/the-machine-learning-toolkit.html

284 Regularization (mathematics) - Regularization in statistics and machine learning 1 Bayesian model comparison|Bayesian learning methods make use of a prior probability that (usually) gives lower probability to more complex models. Well- known model selection techniques include the Akaike information criterion (AIC), minimum description length (MDL), and the Bayesian information criterion (BIC). Alternative methods of controlling overfitting not involving regularization include cross- validation (statistics)|cross-validation. https://store.theartofservice.com/the-machine-learning-toolkit.html

285 Regularization (mathematics) - Regularization in statistics and machine learning 1 Regularization can be used to fine tune model complexity using an augmented error function with cross-validation https://store.theartofservice.com/the-machine-learning-toolkit.html

286 Regularization (mathematics) - Regularization in statistics and machine learning 1 Examples of applications of different methods of regularization to the linear model are: https://store.theartofservice.com/the-machine-learning-toolkit.html

287 Regularization (mathematics) - Regularization in statistics and machine learning 1 A linear combination of the LASSO and ridge regression methods is elastic net regularization. https://store.theartofservice.com/the-machine-learning-toolkit.html

288 Classification in machine learning 1 In machine learning and statistics, 'classification' is the problem of identifying to which of a set of categorical data|categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known https://store.theartofservice.com/the-machine-learning-toolkit.html

289 Classification in machine learning 1 In the terminology of machine learning, classification is considered an instance of supervised learning, i.e. learning where a training set of correctly identified observations is available. The corresponding unsupervised learning|unsupervised procedure is known as cluster analysis|clustering, and involves grouping data into categories based on some measure of inherent similarity or distance. https://store.theartofservice.com/the-machine-learning-toolkit.html

290 Classification in machine learning 1 Often, the individual observations are analyzed into a set of quantifiable properties, known variously explanatory variables, features, etc https://store.theartofservice.com/the-machine-learning-toolkit.html

291 Classification in machine learning 1 An algorithm that implements classification, especially in a concrete implementation, is known as a 'Pattern recognition|classifier'. The term classifier sometimes also refers to the mathematical function (mathematics)|function, implemented by a classification algorithm, that maps input data to a category. https://store.theartofservice.com/the-machine-learning-toolkit.html

292 Classification in machine learning 1 In machine learning, the observations are often known as instances, the explanatory variables are termed features (grouped into a feature vector), and the possible categories to be predicted are classes https://store.theartofservice.com/the-machine-learning-toolkit.html

293 Classification in machine learning - Relation to other problems 1 Classification and clustering are examples of the more general problem of pattern recognition, which is the assignment of some sort of output value to a given input value https://store.theartofservice.com/the-machine-learning-toolkit.html

294 Classification in machine learning - Relation to other problems 1 A common subclass of classification is probabilistic classification https://store.theartofservice.com/the-machine-learning-toolkit.html

295 Classification in machine learning - Relation to other problems 1 *It can output a confidence value associated with its choice (in general, a classifier that can do this is known as a confidence-weighted classifier). https://store.theartofservice.com/the-machine-learning-toolkit.html

296 Classification in machine learning - Relation to other problems 1 *Correspondingly, it can abstain when its confidence of choosing any particular output is too low. https://store.theartofservice.com/the-machine-learning-toolkit.html

297 Classification in machine learning - Relation to other problems 1 *Because of the probabilities which are generated, probabilistic classifiers can be more effectively incorporated into larger machine-learning tasks, in a way that partially or completely avoids the problem of error propagation. https://store.theartofservice.com/the-machine-learning-toolkit.html

298 Classification in machine learning - Frequentist procedures 1 Early work on statistical classification was undertaken by Fisher,R https://store.theartofservice.com/the-machine-learning-toolkit.html

299 Classification in machine learning - Bayesian procedures 1 Unlike frequentist procedures, Bayesian classification procedures provide a natural way of taking into account any available information about the relative sizes of the sub-populations associated with the different groups within the overall population.Binder, D.A https://store.theartofservice.com/the-machine-learning-toolkit.html

300 Classification in machine learning - Bayesian procedures 1 Some Bayesian procedures involve the calculation of class membership probabilities|group membership probabilities: these can be viewed as providing a more informative outcome of a data analysis than a simple attribution of a single group-label to each new observation. https://store.theartofservice.com/the-machine-learning-toolkit.html

301 Classification in machine learning - Binary and multiclass classification 1 Classification can be thought of as two separate problems – binary classification and multiclass classification https://store.theartofservice.com/the-machine-learning-toolkit.html

302 Classification in machine learning - Linear classifiers 1 A large number of algorithms for classification can be phrased in terms of a linear function that assigns a score to each possible category k by linear combination|combining the feature vector of an instance with a vector of weights, using a dot product. The predicted category is the one with the highest score. This type of score function is known as a linear predictor function and has the following general form: https://store.theartofservice.com/the-machine-learning-toolkit.html

303 Classification in machine learning - Linear classifiers 1 where 'X'i is the feature vector for instance i, 'beta;'k is the vector of weights corresponding to category k, and score('X'i, k) is the score associated with assigning instance i to category k. In discrete choice theory, where instances represent people and categories represent choices, the score is considered the utility associated with person i choosing category k. https://store.theartofservice.com/the-machine-learning-toolkit.html

304 Classification in machine learning - Linear classifiers 1 Algorithms with this basic setup are known as linear classifiers. What distinguishes them is the procedure for determining (training) the optimal weights/coefficients and the way that the score is interpreted. https://store.theartofservice.com/the-machine-learning-toolkit.html

305 Classification in machine learning - Linear classifiers 1 Examples of such algorithms are https://store.theartofservice.com/the-machine-learning-toolkit.html

306 Classification in machine learning - Linear classifiers 1 *Logistic regression and multinomial logit https://store.theartofservice.com/the-machine-learning-toolkit.html

307 Classification in machine learning - Algorithms 1 Examples of classification algorithms include: https://store.theartofservice.com/the-machine-learning-toolkit.html

308 Classification in machine learning - Algorithms 1 **Least squares support vector machines https://store.theartofservice.com/the-machine-learning-toolkit.html

309 Classification in machine learning - Algorithms 1 * Variable kernel density estimation#Use for statistical classification|Kernel estimation https://store.theartofservice.com/the-machine-learning-toolkit.html

310 Classification in machine learning - Evaluation 1 Classifier performance depends greatly on the characteristics of the data to be classified https://store.theartofservice.com/the-machine-learning-toolkit.html

311 Classification in machine learning - Evaluation 1 The measures precision and recall are popular metrics used to evaluate the quality of a classification system. More recently, receiver operating characteristic (ROC) curves have been used to evaluate the tradeoff between true- and false- positive rates of classification algorithms. https://store.theartofservice.com/the-machine-learning-toolkit.html

312 Classification in machine learning - Evaluation 1 As a performance metric, the uncertainty coefficient has the advantage over simple accuracy in that it is not affected by the relative sizes of the different classes. https://store.theartofservice.com/the-machine-learning-toolkit.html

313 Classification in machine learning - Evaluation 1 Further, it will not penalize an algorithm for simply rearranging the classes. https://store.theartofservice.com/the-machine-learning-toolkit.html

314 Classification in machine learning - Application domains 1 Classification has many applications. In some of these it is employed as a data mining procedure, while in others more detailed statistical modeling is undertaken. https://store.theartofservice.com/the-machine-learning-toolkit.html

315 Classification in machine learning - Application domains 1 * Drug discovery and Drug development|development https://store.theartofservice.com/the-machine-learning-toolkit.html

316 Classification in machine learning - Application domains 1 ** Quantitative structure-activity relationship https://store.theartofservice.com/the-machine-learning-toolkit.html

317 Classification in machine learning - Application domains 1 * Statistical natural language processing https://store.theartofservice.com/the-machine-learning-toolkit.html

318 Classification in machine learning - Application domains 1 * Document classification https://store.theartofservice.com/the-machine-learning-toolkit.html

319 Cognitive bias mitigation - Machine learning 1 Machine learning, a branch of artificial intelligence, has been used to investigate human learning and decision making.Sutton, R. S., Barto, A. G. (1998). MIT CogNet Ebook Collection; MITCogNet 1998, Adaptive Computation and Machine Learning, ISBN https://store.theartofservice.com/the-machine-learning-toolkit.html

320 Cognitive bias mitigation - Machine learning 1 One technique particularly applicable to Cognitive Bias Mitigation is neural network|neural network learning and choice selection, an approach inspired by the imagined structure and function of actual neural networks in the human brain https://store.theartofservice.com/the-machine-learning-toolkit.html

321 Cognitive bias mitigation - Machine learning 1 In principle, such models are capable of modeling decision making that takes account of human needs and motivations within social contexts, and suggest their consideration in a theory and practice of Cognitive Bias Mitigation https://store.theartofservice.com/the-machine-learning-toolkit.html

322 ConceptNet - Machine learning tools 1 The information in ConceptNet can be used as a basis for machine learning algorithms. One representation, called AnalogySpace, uses singular value decomposition to generalize and represent patterns in the knowledge in https://store.theartofservice.com/the-machine-learning-toolkit.html

323 ConceptNet - Machine learning tools 1 ConceptNet, in a way that can be used in AI applications. Its creators distribute a Python machine learning toolkit called Divisi for performing machine learning based on text corpora, structured knowledge bases such as ConceptNet, and combinations of the two. https://store.theartofservice.com/the-machine-learning-toolkit.html

324 Learning algorithms - Machine learning and data mining 1 * Machine learning focuses on prediction, based on known properties learned from the training data. https://store.theartofservice.com/the-machine-learning-toolkit.html

325 Learning algorithms - Machine learning and data mining 1 * Data mining focuses on the discovery (observation)|discovery of (previously) unknown properties in the data. This is the analysis step of Knowledge discovery|Knowledge Discovery in Databases. https://store.theartofservice.com/the-machine-learning-toolkit.html

326 Learning algorithms - Machine learning and data mining 1 Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in Knowledge Discovery and Data Mining (KDD) the key task is the discovery of previously unknown knowledge https://store.theartofservice.com/the-machine-learning-toolkit.html

327 Classification (machine learning) - Feature vectors 1 Most algorithms describe an individual instance whose category is to be predicted using a feature vector of individual, measurable properties of the instance https://store.theartofservice.com/the-machine-learning-toolkit.html

328 Classification (machine learning) - Feature vectors 1 The vector space associated with these vectors is often called the feature space. In order to reduce the dimensionality of the feature space, a number of dimensionality reduction techniques can be employed. https://store.theartofservice.com/the-machine-learning-toolkit.html

329 Ground truth - Statistics and Machine Learning 1 In machine learning, the term ground truth refers to the accuracy of the training set's classification for supervised learning techniques. This is used in statistical models to prove or disprove research hypothesis|hypotheses. The term ground truthing refers to the process of gathering the proper objective data for this test. Compare with gold standard (test). https://store.theartofservice.com/the-machine-learning-toolkit.html

330 Ground truth - Statistics and Machine Learning 1 Bayesian spam filtering is a common example of supervised learning. In this system, the algorithm is manually taught the differences between spam and non- spam. This depends on the ground truth of the messages used to train the algorithm; inaccuracies in that ground truth will correlate to inaccuracies in the resulting spam/non-spam verdicts. https://store.theartofservice.com/the-machine-learning-toolkit.html

331 For More Information, Visit: https://store.theartofservice.co m/the-machine-learning- toolkit.html https://store.theartofservice.co m/the-machine-learning- toolkit.html The Art of Service https://store.theartofservice.com


Download ppt "Machine Learning https://store.theartofservice.com/the-machine-learning-toolkit.html."

Similar presentations


Ads by Google