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ETHEM ALPAYDIN The MIT Press, 2010
 

Summary: ETHEM ALPAYDIN
© The MIT Press, 2010
alpaydin@boun.edu.tr
http://www.cmpe.boun.edu.tr/~ethem/i2ml2e
Lecture Slides for
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 boundaries
3Lecture Notes for E Alpaydin 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)
Linear discriminant:
Advantages:
Simple: O(d) space/computation
Knowledge extraction: Weighted sum of attributes;
positive/negative weights, magnitudes (credit scoring)

  

Source: Alpaydın, Ethem - Department of Computer Engineering, Bogaziçi University

 

Collections: Computer Technologies and Information Sciences