S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks ECE /ECE Fall 2010 Shreekanth Mandayam ECE Department Rowan University Lecture 5 October 11, 2010
S. Mandayam/ ANN/ECE Dept./Rowan UniversityPlan The “Optimal” Classifier Bayes Classifier Multilayer Perceptron Improvements to the backpropagation algorithm Input data processing Selection of training and test data - cross- validation Pre-processing: Feature Extraction Lab Project 2
S. Mandayam/ ANN/ECE Dept./Rowan University Recall: Backpropagation Forward Pass Fix w ji (n) Compute y j (n) Backward Pass Calculate j (n) Update weights w ji (n+1) i j k Left Right i j k Left Right x y
S. Mandayam/ ANN/ECE Dept./Rowan University Bayes Classifier for a 2-Class Problem See Dr. Robi Polikar’s Pattern Recognition ECE /555
S. Mandayam/ ANN/ECE Dept./Rowan UniversityIllustration
S. Mandayam/ ANN/ECE Dept./Rowan University Bayes Classifier for a 2-Class Problem See Mike Muhlbaier’s MLP demo demos/NNdemo
S. Mandayam/ ANN/ECE Dept./Rowan University Backprop: “Improvements” pp Sequential vs. Batch update Maximizing information content Choice of activation function Target values Normalizing inputs Initialization Learning from “hints” Variation in learning rates
S. Mandayam/ ANN/ECE Dept./Rowan University Selection of Training and Test Data: Method of Cross-Validation Train Test Train TestTrain TestTrain TestTrain Trial 1 Trial 2 Trial 3 Trial 4 Vary network parameters until total mean squared error is minimum for all trials Find network with the least mean squared output error
S. Mandayam/ ANN/ECE Dept./Rowan University Feature Extraction Objective: Increase information content Decrease vector length Parametric invariance Invariance by structure Invariance by training Invariance by transformation
S. Mandayam/ ANN/ECE Dept./Rowan University Lab Project 2 /fall10/ann/lab2.htmlhttp://engineering.rowan.edu/~shreek /fall10/ann/lab2.html
S. Mandayam/ ANN/ECE Dept./Rowan UniversitySummary