Pattern Recognition and Application

IIT Kharagpur, , Prof. P.K. Biswas

Updated On 02 Feb, 19

Overview

The course has been designed to be offered as an elective to final year under graduate students mainly from Electrical Sciences background. The course syllabus assumes basic knowledge of Signal Processing, Probability Theory and Graph Theory. The course will also be of interest to researchers working in the areas of Machine Vision, Speech Recognition, Speaker Identification, Process Identification etc.

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Lecture 27: RBF Neural Network

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Lecture Details

Pattern Recognition and Application by Prof. P.K. Biswas,Department of Electronics & Communication Engineering,IIT Kharagpur.For more details on NPTEL visit httpnptel.ac.in

Course Details

COURSE LAYOUT

Week 1 : Introduction
  Feature Extraction - I
  Feature Extraction - II

Week 2 : Bayes Decision Theory - I
Bayes Decision Theory - II

Week 3 :  Normal Density and Discriminant Function - I
  Normal Density and Discriminant Function - II
  Bayes Decision Theory - Binary Features

Week 4 : Maximum Likelihood Estimation
Probability Density Estimation - I

Week 5 : Probability Density Estimation - II
  Probability Density Estimation - III
  Probability Density Estimation - IV

Week 6 : Dimensionality Problem
  Multiple Discriminant Analysis

Week 7 : Principal Component Analysis - Tutorial
  Multiple Discriminant Analysis - Tutorial
  Perceptron Criteria - I

Week 8 : Perceptron Criteria - II
MSE Criteria

Week 9 : Linear Discriminator Tutorial
  Neural Network - I
  Neural Network - II

Week 10 : Neural Network -III/ Hopefield Network
  RBF Neural Network - I

Week 11 : RBF Neural Network - II
  Support Vector Machine
Clustering -I

Week 12 : Clustering -II
Clustering -III