pattern recognation material

PATTEREN RECOGNITION


UNIT-V
Un-supervised learning and clustering : Introduction, mixture densities and identifiability, maximum
likelihood estimates, application to normal mixtures, K-means clustering. Date description and clustering –
similarity measures, criteria function for clustering.
UNIT-VI
Component analyses : Principal component analysis, non-linear component analysis; Low dimensional
representations and multi dimensional scaling.
UNIT-VII
Discrete Hidden Morkov Models : Introduction, Discrete–time markov process, extensions to hidden
Markov models, three basic problems for HMMs.
UNIT-VIII
Continuous hidden Markov models : Observation densities, training and testing with continuous HMMs,
types of HMMs.

REFERENCE :
1. Pattern Recognition and Image Analysis – Earl Gose, Richard John baugh, Steve Jost PHI 2004

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