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Pattern Recognition and Application

Pattern Recognition and Application. Instructor: Prof. P. K. Biswas, Department of Electronics and Communication Engineering, IIT Kharagpur. This course covers feature extraction techniques and representation of patterns in feature space. Measure of similarity between two patterns. Statistical, nonparametric and neural network techniques for pattern recognition have been discussed in this course. Techniques for recognition of time varying patterns have also been covered. Numerous examples from machine vision, speech recognition and movement recognition have been discussed as applications. Unsupervised classification or clustering techniques have also been addressed in this course. Analytical aspects have been adequately stressed so that on completion of the course the students can apply the concepts learnt in real life problems. (from nptel.ac.in)

Lecture 33 - Reflex Min-Max Neural Network


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Lecture 01 - Introduction
Feature Extraction
Lecture 02 - Feature Extraction I
Lecture 03 - Feature Extraction II
Lecture 04 - Feature Extraction III
Statistical Pattern Recognition
Lecture 05 - Bayes Decision Theory
Lecture 06 - Bayes Decision Theory (cont.)
Lecture 07 - Normal Density and Discriminant Function
Lecture 08 - Normal Density and Discriminant Function (cont.)
Lecture 09 - Bayes Decision Theory - Binary Features
Maximum Likelihood Estimation
Lecture 10 - Maximum Likelihood Estimation
Probability Density Estimation
Lecture 11 - Probability Density Estimation
Lecture 12 - Probability Density Estimation (cont.)
Lecture 13 - Probability Density Estimation (cont.)
Lecture 14 - Probability Density Estimation (cont.)
Lecture 15 - Probability Density Estimation (cont.)
Dimensionality Problem
Lecture 16 - Dimensionality Problem
Lecture 17 - Multiple Discriminant Analysis
Lecture 18 - Multiple Discriminant Analysis (Tutorial)
Lecture 19 - Multiple Discriminant Analysis (Tutorial)
Linear Discriminant Functions
Lecture 20 - Perceptron Criterion
Lecture 21 - Perceptron Criterion (cont.)
Lecture 22 - Minimum Square Error Criterion
Lecture 23 - Linear Discriminator (Tutorial)
Neural Network Classifier
Lecture 24 - Neural Networks for Pattern Recognition
Lecture 25 - Neural Networks for Pattern Recognition (cont.)
Lecture 26 - Neural Networks for Pattern Recognition (cont.)
Lecture 27 - RBF (Radial Basis Function) Neural Network
Lecture 28 - RBF (Radial Basis Function) Neural Network (cont.)
Lecture 29 - Support Vector Machine
Lecture 30 - Hyperbox Classifier
Lecture 31 - Hyperbox Classifier (cont.)
Lecture 32 - Fuzzy Min-Max Neural Network for Pattern Recognition
Lecture 33 - Reflex Min-Max Neural Network
Unsupervised Classification
Lecture 34 - Unsupervised Learning - Clustering
Lecture 35 - Clustering (cont.)
Lecture 36 - Clustering using Minimum Spanning Tree
Time Varying Pattern Recognition
Lecture 37 - Temporal Pattern Recognition
Lecture 38 - Hidden Markov Model
Lecture 39 - Hidden Markov Model (cont.)
Lecture 40 - Hidden Markov Model (cont.)