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Intelligent Environments1 Computer Science and Engineering University of Texas at Arlington

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Intelligent Environments2 Prediction for Intelligent Environments Motivation Techniques Issues

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Intelligent Environments3 Motivation An intelligent environment acquires and applies knowledge about you and your surroundings in order to improve your experience. “acquires” prediction “applies” decision making

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Intelligent Environments4 What to Predict Inhabitant behavior Location Task Action Environment behavior Modeling devices Interactions

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Intelligent Environments5 Example Where will Bob go next? Location t+1 = f(…) Independent variables Location t, Location t-1, … Time, date, day of the week Sensor data Context Bob’s task

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Intelligent Environments6 Example (cont.) TimeDateDayLocation t Location t /25MondayBedroomBathroom /25MondayBathroomKitchen /25MondayKitchenGarage /25MondayGarageKitchen /25MondayKitchenBedroom /25MondayBedroomLiving room /25MondayLiving roomBathroom /25MondayBathroomBedroom /26TuesdayBedroomBathroom

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Intelligent Environments7 Example Learned pattern If Day = Monday…Friday & Time > 0600 & Time < 0700 & Location t = Bedroom Then Location t+1 = Bathroom

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Intelligent Environments8 Prediction Techniques Regression Neural network Nearest neighbor Bayesian classifier Decision tree induction Others

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Intelligent Environments9 Linear Regression xy

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Intelligent Environments10 Multiple Regression n independent variables Find b i System of n equations and n unknowns

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Intelligent Environments11 Regression Pros Fast, analytical solution Confidence intervals y = a ± b with C% confidence Piecewise linear and nonlinear regression Cons Must choose model beforehand Linear, quadratic, … Numeric variables

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Intelligent Environments12 Neural Networks

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Intelligent Environments13 Neural Networks synapses per neuron Synapses propagate electrochemical signals Number, placement and strength of connections changes over time (learning?) Massively parallel

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Intelligent Environments14 Computer vs. Human Brain ComputerHuman Brain Computational units1 CPU, 10 8 gates10 11 neurons Storage units10 10 bits RAM, bits disk neurons, synapses Cycle time10 -9 sec10 -3 sec Bandwidth10 9 bits/sec10 14 bits/sec Neuron updates / sec

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Intelligent Environments15 Computer vs. Human Brain “The Age of Spiritual Machines,” Kurzweil.

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Intelligent Environments16 Artificial Neuron

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Intelligent Environments17 Artificial Neuron Activation functions

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Intelligent Environments18 Perceptron

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Intelligent Environments19 Perceptron Learning

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Intelligent Environments20 Perceptron Learns only linearly-separable functions

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Intelligent Environments21 Sigmoid Unit

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Intelligent Environments22 Multilayer Network of Sigmoid Units

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Intelligent Environments23 Error Back-Propagation Errors at output layer propagated back to hidden layers Error proportional to link weights and activation Gradient descent in weight space

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Intelligent Environments24 NN for Face Recognition 90% accurate learning head pose for 20 different people.

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Intelligent Environments25 Neural Networks Pros General purpose learner Fast prediction Cons Best for numeric inputs Slow training Local optima

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Intelligent Environments26 Nearest Neighbor Just store training data (x i,f(x i )) Given query x q, estimate using nearest neighbor x k : f(x q ) = f(x k ) k nearest neighbor Given query x q, estimate using majority (mean) of k nearest neighbors

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Intelligent Environments27 Nearest Neighbor

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Intelligent Environments28 Nearest Neighbor Pros Fast training Complex target functions No loss of information Cons Slow at query time Easily fooled by irrelevant attributes

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Intelligent Environments29 Bayes Classifier Recall Bob example D = training data h = sample rule

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Intelligent Environments30 Naive Bayes Classifier Naive Bayes assumption Naive Bayes classifier y represents Bob’s location

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Intelligent Environments31 Bayes Classifier Pros Optimal Discrete or numeric attribute values Naive Bayes easy to compute Cons Bayes classifier computationally intractable Naive Bayes assumption usually violated

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Intelligent Environments32 Decision Tree Induction Day Time > 0600 Location t Time < 0700 Bathroom M…F yes Bedroom … no Sat Sun

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Intelligent Environments33 Decision Tree Induction Algorithm (main loop) 1. A = best attribute for next node 2. Assign A as attribute for node 3. For each value of A, create descendant node 4. Sort training examples to descendants 5. If training examples perfectly classified, then Stop, else iterate over descendants

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Intelligent Environments34 Decision Tree Induction Best attribute Based on information-theoretic concept of entropy Choose attribute reducing entropy (~uncertainty) from parent to descendant nodes A1A2 Bathroom (0) Kitchen (50) Bathroom (50) Kitchen (0) Bathroom (25) Kitchen (25) Bathroom (25) Kitchen (25) ??BK v2v2 v1v1 v1v1 v2v2

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Intelligent Environments35 Decision Tree Induction Pros Understandable rules Fast learning and prediction Cons Replication problem Limited rule representation

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Intelligent Environments36 Other Prediction Methods Hidden Markov models Radial basis functions Support vector machines Genetic algorithms Relational learning

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Intelligent Environments37 Prediction Issues Representation of data and patterns Relevance of data Sensor fusion Amount of data

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Intelligent Environments38 Prediction Issues Evaluation Accuracy False positives vs. false negatives Concept drift Time-series prediction Distributed learning

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