1. Pattern Recognition

2. What is Pattern Recognition? Pattern recognition is a sub-topic of machine learning. PR is the science that concerns the description or classification (recognition) of measurements. It can be defined as:  “The act of taking in raw data and taking an action based on the category of the data".  “The assignment of a physical object or event to one of several prespecified categories”.

3. A pattern is an object, process or event that can be given a name. A pattern class (or category) is a set of patterns sharing common attributes and usually originating from the same source. During recognition (or classification) given objects are assigned to prescribed classes. A classifier is a machine which performs classification.

4. Pattern recognition system A complete pattern recognition system consists of :- Sensor -gathers the observations to be classified or described. Sensor Feature extraction mechanism -computes numeric or symbolic information from the observations.Feature extraction Classification or description scheme -that does the actual job of classifying or describing observations, relying on the extracted features. Classification

5. Algorithms used by pattern recognition systems DESCRIPTIONCLASSIFICATION PATTERN RECOGNITION ALGORITHMS data identification features

6. Description task The description task transforms data collected from the environment into features. The description task can involve several different, but interrelated, activities: Preprocessing:-To modify the data Feature extraction:-To generate features -- Elementary features -- Higher order features Feature selection:-To reduce features

7. Description task (cont.) The end result of the description task is a set of features, commonly called a feature vector which constitutes a representation of the data.

8. Classification task Uses a classifier to map a feature vector to a group. Such a mapping can be specified by hand or, more commonly, a training phase is used to induce the mapping from a collection of feature vectors known to be representative of the various groups among which discrimination is being performed (i.e., the training set). Once formulated, the mapping can be used to assign an identification to each unlabeled feature vector subsequently presented to the classifier.

9. What makes a ”good” feature vector

10. Approaches to pattern recognition There are 2 fundamental approaches to implement a pattern recognition system: 1.Statistical (or decision theoretic):-Statistical pattern recognition is based on statistical characterizations of patterns, assuming that the patterns are generated by a probabilistic system. 2. Syntactic (or structural):-Syntactical pattern recognition is based on the structural interrelationships of features.

11. Statistical pattern recognition It draws from established concepts in statistical decision theory to discriminate among data from different groups based upon quantitative features of the data. There are a wide variety of statistical techniques that can be used within the description task for feature extraction, ranging from simple descriptive statistics to complex transformations.

12. Syntactic pattern recognition Syntactic pattern recognition or structural pattern recognition is a form of pattern recognition, where items are presented pattern structures which can take into account more complex interrelationships between features than simple numerical feature vectors used in statistical classification. It can be used (instead of statistical pattern recognition) if there is clear structure in the patterns. One way to present such structure is strings of a formal language. In this case differences in the structures of the classes are encoded as different grammars.

13. Approaches to pattern recognition

14. Difference Between Statistical and Structural Pattern Recognition StatisticalStructural FoundationStatistical decision theoryHuman perception and cognition DescriptionQuantative features Fixed no. of features Ignores feature relationships Semantics from feature position Morphological primitives Variable number of primitives Captures primitive relationships Semantics from primitive encoding ClassificationStatistical classifiersParsing with syntactic grammars

15. Neural networks pattern recognition An “Artificial Neural Network" (ANN), is a mathematical model or computational model based on biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. In more practical terms neural networks are non- linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.

16. Neural networks pattern recognition Classification is based on the response of a network of processing units(neurons) to an input stimuli (pattern). “Knowledge” is stored in the connectivity and strength of the synaptic weights. NeurPR is a trainable, non-algorithmic, black-box strategy. NeurPR is very attractive since -it requires minimum a priori knowledge -with enough layers and neurons, an ANN can create any complex decision region.