28-02-2013, 09:19 AM
11MS028 PATTERN RECOGNITION SYLLABUS
RECOGNITION SYLLABUS.docx (Size: 13.73 KB / Downloads: 22)
MODULE - I
Basics of Pattern Recognition: Machine perception – Pattern recognition system – Design cycle – learning and adaptation - Bayesian decision theory - Classifiers, Discriminant functions, Decision surfaces - Normal density and discriminant functions – Error Probabilities and Integrals – Error bounds –Discrete features – Missing and noisy features – Bayesian Belief Networks.
MODULE - II
Parameter Estimation: Parameter estimation methods Maximum-Likelihood estimation - Gaussian mixture models - Expectation-maximization method - Bayesian estimation - Hidden Markov models for sequential pattern classification - Discrete hidden Markov models - Continuous density hidden Markov models - Dimension reduction methods - Fisher discriminant analysis - Principal component analysis - Non-parametric techniques for density estimation - Parzen-window method - K-Nearest Neighbor method
MODULE - III
Classification and Clustering
Linear discriminant function based classifiers - Perceptron - Support vector machines - Non-metric methods for pattern classification - Non-numeric data or nominal data - Decision trees - Unsupervised learning and clustering - Criterion functions for clustering - Algorithms for clustering: K-means, Hierarchical and other methods- Cluster validation.