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Introduction to Machine Learning Lecture 11

Introduction to Machine LearningLecture 11 Mehryar MohriCourant Institute and Google Mohri - Introduction to Machine Learning2 BoostingpageMehryar Mohri - Introduction to Machine Learning3 Main idea: use weak learner to create strong method: combine base classifiers returned by weak simple relatively accurate base classifiers often not , how should base classifiers be combined?Boosting IdeaspageMehryar Mohri - Introduction to Machine LearningAdaBoost4 AdaBoost(S=((x1,y1),..,(xm,ym)))1fori 1tomdo2D1(i) 1m3fort 1toTdo4ht base classifier inHwith small error t=PrDt[ht(xi) =yi]5 t 12log1 t t6Zt 2[ t(1 t)]12 normalization factor7fori 1tomdo8Dt+1(i) Dt(i)exp( tyiht(xi))Zt9f Tt=1 tht10returnh=sgn(f)H { 1,+1}X.

Mehryar Mohri - Introduction to Machine Learning page Notes Distributions over training sample: • originally uniform. • at each round, the weight of a misclassified example is increased. • observation: , since Weight assigned to base classifier : directy

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