06CS664 - Pattern Recognition |
PART – A |
UNIT 1 |
INTRODUCTION: Machine perception, an example; Pattern Recognition
System; The Design Cycle; Learning and Adaptation. |
UNIT 2 |
BAYESIAN DECISION THEORY: Introduction, Bayesian Decision
Theory; Continuous Features, Minimum error rate, classification, classifiers,
discriminant functions, and decision surfaces; The normal density;
Discriminant functions for the normal density. |
UNIT 3 |
MAXIMUM-LIKELIHOOD AND BAYESIAN PARAMETER
ESTIMATION: Introduction; Maximum-likelihood estimation; Bayesian
Estimation; Bayesian parameter estimation: Gaussian Case, general theory;
Hidden Markov Models. |
UNIT 4 |
NON-PARAMETRIC TECHNIQUES: Introduction; Density Estimation;
Parzen windows; kn – Nearest- Neighbor Estimation; The Nearest- Neighbor
Rule; Metrics and Nearest-Neighbor Classification. |
PART – B |
UNIT 5 |
LINEAR DISCRIMINANT FUNCTIONS: Introduction; Linear
Discriminant Functions and Decision Surfaces; Generalized Linear
Discriminant Functions; The Two-Category Linearly Separable case;
Minimizing the Perception Criterion Functions; Relaxation Procedures; Nonseparable
Behavior; Minimum Squared-Error procedures; The Ho-Kashyap
procedures. |
UNIT 6 |
STOCHASTIC METHODS: Introduction; Stochastic Search; Boltzmann
Learning; Boltzmann Networks and Graphical Models; Evolutionary
Methods. |
UNIT 7 |
NON-METRIC METHODS: Introduction; Decision Trees; CART; Other
Tree Methods; Recognition with Strings; Grammatical Methods. |
UNIT 8 |
UNSUPERVISED LEARNING AND CLUSTERING: Introduction;
Mixture Densities and Identifiability; Maximum-Likelihood Estimates;
Application to Normal Mixtures; Unsupervised Bayesian Learning; Data
Description and Clustering; Criterion Functions for Clustering. |
REFERENCE |
TEXT BOOKS: |
1. Pattern Classification – Richard O. Duda, Peter E. Hart, and David
G.Stork:, 2nd Edition, Wiley-Interscience, 2001.
|
Reference Books |
1. Pattern Recognition and Image Analysis – Earl Gose – Richard
Johnsonbaugh, Steve Jost – Pearson Education, 2007. |