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You are here:Open notes-->VTU-->PATTERN-RECOGNITION-10CS664-

**PATTERN RECOGNITION 10CS664 **

# How to study this subject

Subject Code: 10CS664 I.A. Marks : 25

Hours/Week : 04 Exam Hours: 03

Total Hours : 52 Exam Marks: 100

PART – A

**UNIT – 1 6 Hours**

Introduction: Machine perception, an example; Pattern Recognition System;

The Design Cycle; Learning and Adaptation.

**UNIT – 2 7 Hours**

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 7 Hours**

Maximum-likelihood and Bayesian Parameter Estimation: Introduction;

Maximum-likelihood estimation; Bayesian Estimation; Bayesian parameter

estimation: Gaussian Case, general theory; Hidden Markov Models.

**UNIT – 4 6 Hours**

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 7 Hours**

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.

**UNIT – 6 6 Hours**

Stochastic Methods: Introduction; Stochastic Search; Boltzmann Learning;

Boltzmann Networks and Graphical Models; Evolutionary Methods.

**UNIT – 7 6 Hours**

Non-Metric Methods: Introduction; Decision Trees; CART; Other Tree

Methods; Recognition with Strings; Grammatical Methods. 61

**UNIT – 8 7 Hours**

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.

**Text Books:**

1. Richard O. Duda, Peter E. Hart, and David G.Stork: Pattern

Classification, 2nd Edition, Wiley-Interscience, 2001.

**Reference Books:**

1. Earl Gose, Richard Johnsonbaugh, Steve Jost: Pattern Recognition

and Image Analysis, PHI, Indian Reprint 2008.

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