Pattern Recognition and Application

Overview

Introduction:Feature extraction and Pattern Representation - Concept of Supervised and Unsupervised Classification - Introduction to Application Areas;Statistical Pattern Recognition:Bayes Decision Theory - Minimum Error and Minimum Risk Classifiers - Discriminant Function and Decision Boundary - Normal Density - Discriminant Function for Discrete Features - Parameter Estimation;Dimensionality Problem:Dimension and accuracy - Computational Complexity - Dimensionality Reduction - Fisher Linear Discriminant - Multiple Discriminant Analysis;Nonparametric Pattern Classification:Density Estimation - Nearest Neighbour Rule - Fuzzy Classification;Linear Discriminant Functions:Separability - Two Category and Multi Category Classification - Linear Discriminators - Perceptron Criterion - Relaxation Procedure - Minimum Square Error Criterion - Widrow-Hoff Procedure - Ho-Kashyap Procedure - Keslers Construction;Neural Network Classifier:Single and Multilayer Perceptron - Back Propagation Learning - Hopfield Network - Fuzzy Neural Network;Time Varying Pattern Recognition:First Order Hidden Markov Model - Evaluation - Decoding - Learning;Unsupervised Classification:Clustering - Hierarchical Clustering - Graph Based Method - Sum of Squared Error Technique - Iterative Optimization