BM2401 PATTERN RECOGNITION AND NEURAL NETWORKS Lecture Notes for BME - Seventh (7th) semester - R.Anirudhan

BM2401 PATTERN RECOGNITION AND NEURAL NETWORKS Lecture Notes for BME - Seventh (7th) semester


BM2401 Lecture Notes 

Syllabus :

UNIT I INTRODUCTION AND SIMPLE NEURAL NET 
Elementary neurophysiology and biological neural network-Artificial neural network –
Architecture, biases and thresholds, Hebb net, Perceptron, Adaline and Madaline.

UNIT II BACK PROPOGATION AND ASSOCIATIVE MEMORY 
Back propogation network, generalized delta rule, Bidirectional Associative memory,
Hopefield network

UNIT III NEURAL NETWORKS BASED ON COMPETITION 
Kohonen Self organising map, Learning Vector Quantisation, counter propogation
network.

UNIT IV UNSUPERVISED LEARNING AND CLUSTERING ANALYSIS 
Patterns and features, training and learning in pattern recognition, discriminant functions,
different types of pattern recognition. Unsupervised learning- hierarchical clustering,
partitional clustering. Neural pattern recognition approach – perceptron model

UNIT V SUPERVISED LEARNING USING PARAMETRIC AND NON 
PARAMETRIC APPROACH
Bayesian classifier, non parametric density estimation, histograms, kernels, window
estimators, k-nearest neighbour classifier , estimation of error rates.


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