Saturday, June 30, 2012
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.
download Pattern Recognition and Neural Networks Lecture Notes
By Vinoth
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