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VTU-M.TECH. COMPUTER ENGINEERING-Pattern Classification
Posted Date: 12 Sep 2008 Resource Type: Articles/Knowledge Sharing Category: Syllabus
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Posted By: Lenin Member Level: Diamond
Rating:  Points: 2
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Pattern Classification
Subject Code: 08SCE332 I.A. Marks: 50
Hours/Week: 4 Exam Hours: 03
Total Hours: 52 Exam Marks: 100
1. Introduction
Machine perception, Pattern Recognition Systems, The Design Cycle;
Learning and Adaptation.
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, Error Probabilities and Integrals, Error Bounds for
Normal Densities, Bayes Decision Theory: Discrete Features.
3. Maximum-Likelihood and Bayesian Parameter Estimation
Introduction; Maximum-likelihood estimation; Bayesian Estimation;
Bayesian Parameter Estimation: Gaussian Case, general theory; Sufficient
Statistics; Problems of Dimensionality; Component Analysis and
Discriminants.
4. Non-Parametric Techniques
Introduction; Density Estimation; Parzen Windows; kn – Nearest-
Neighbor Estimation; The Nearest- Neighbor Rule; Metrics and Nearest-
Neighbor Classification.
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; Non-separable Behavior; Minimum Squared-Error
procedures; The Ho-Kashyap procedures.
6. Stochastic Methods
Introduction; Stochastic Search; Boltzmann Learning; Boltzmann
Networks and Graphical Models; Evolutionary Methods.
7. Unsupervised Learning and Clustering
Introduction; Mixture Densities and Identifiability; Maximum-Likelihood
Estimates; Application to Normal Mixtures; Unsupervised Bayesian
Learning; Data Discrimination and Clustering; Criterion Functions for
Clustering; Iterative Optimization; Hierrchical Clustring; The Problem of
Validity; On-Line Clustering; Graph Theoritic Methods; Low-
Dimensional Representation and Multi-Dimensional Scaling.
8. Introduction to Biometric Recognition
Biometric Methodologies: Finger Prints; Hand Geometry; Facial
Recognition; Iris Scanning; Retina Scanning; Identification versus
Verification; Performance Criteria.
Text Books:
1. Richard O. Duda, Peter E. Hart, and David G.Stork: Pattern Classification,
2nd Edition, Wiley-Interscience, 2001.
2. K. Jain, R. Bolle, S. Pankanti: Biometrics: Personal Identification in
Networked Society, Kluwer Academic, 1999.
Reference Books:
1. Earl Gose, Richard Johnsonbaugh, Steve Jost : Pattern Recognition and
Image Analysis,
Pearson Education, 2007.
For more details, visit http://www.vtu.ac.in
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