Farag - Pattern Recognition - Lecture 3- Discriminant Analysis

# Farag Pattern - PATTERN RECOGNITION Lecture 3 Discriminant Function for Gaussian Distributions Professor Aly A Farag Computer Vision and Image

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PATTERN RECOGNITION Professor Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville URL: www.cvip.uofl.edu ; E-mail: [email protected] [email protected]_______ Planned for ECE 620 and ECE 655 - Summer 2011 TA/Grader: Melih Aslan; CVIP Lab Rm 6, [email protected] Lecture 3: Discriminant Function for Gaussian Distributions

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Discriminant Functions for the Normal Density Recall that the minimum error-rate classification can be achieved by the discriminant function g i (x) = ln P(x | i ) + ln P( i ) It is straight forward to show that g i (x) for, multivariate normal, has the following form: Pattern Classification, Chapter 2 (Part 3) 1 ) ( ln ln 2 1 2 ln 2 ) ( ) ( 2 1 ) ( 1 i i i i t i i P d x x x g
Case i = 2 I ( I stands for the identity matrix) We may express g i (x) in the following form: Pattern Classification, Chapter 2 (Part 3)

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## This note was uploaded on 01/12/2012 for the course ECE 620 taught by Professor Staff during the Summer '08 term at University of Louisville.

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Farag Pattern - PATTERN RECOGNITION Lecture 3 Discriminant Function for Gaussian Distributions Professor Aly A Farag Computer Vision and Image

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