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INTRODUCTION TO Machine Learning - Computer Science

INTRODUCTION TOMachine LearningETHEM ALPAYDIN The MIT Press, Slides forCHAPTER 10:Linear DiscriminationLecture Notes for E Alpayd n 2004 INTRODUCTION to Machine Learning The MIT Press ( )3 Likelihood- vs. Discriminant-based Classification Likelihood-based:Assume a model for p(x|Ci), use Bayes rule to calculate P(Ci|x) gi(x) = log P(Ci|x) Discriminant-based:Assume a model for gi(x| i); no density estimation Estimating the boundaries is enough; no need to accurately estimate the densities inside the boundariesLecture Notes for E Alpayd n 2004 INTRODUCTION to Machine Learning The MIT Press ( )4 Linear Discriminant Linear discriminant: Advantages: Simple: O(d) space/computation Knowledge extraction: Weighted sum of attributes; positive/negative weights, magnitudes (cr)

Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.0) 3 Likelihood- vs. Discriminant-based Classification

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