Introduction to Statistical Learning Theory
Introductionto StatisticalLearningTheoryOlivierBousquet 1, St ephaneBoucheron2, andG aborLugosi31Max-Planck ,D-72076T e deParis-Sud,Laboratoired'InformatiqueB^a timent 490,F-91405Orsay tostudy, ina sta-tisticalframework, ,mostresultstake statisticallearningtheoryis toprovidea frameworkforstudy-ingtheproblemofinferen ce,thatis ofgainingknowledge,makingpredictions,mak ingdecisionsorconstructingmodelsfroma studiedinastatisticalframework,thatis thereareassumptionsofstatisticalnatureab outtheunderlyingphenomena(intheway thedatais generated).Asa motivationfortheneedofsuch a Theory , :(Vapnik,[1]) Nothingis morepracticalthana good ,a theoryofinferenceshouldbe abletogive a formalde nitionofwordslike Learning ,generalization,over tting,andalsotocharacterizetheperformanc eoflearningalgorithmssothat,ultimately, itmay goals:make thingsmorepreciseandderive understudyhereis theprocessof inductive inferencewhich canroughlybe summarizedasthefollowingsteps:176Bousque t,Boucheron& a predictionsusingthismodelOfcourse,thisde nitionis verygeneralandcouldbe toactuallyautomatethisprocessandthegoalo fLearningTheoryis considera specialcaseoftheabove processwhich is ,thedataconsistsof instance-label pairs,wherethelabel is either+1or 1.
Introduction to Statistical Learning Theory Olivier Bousquet1, St ephane Boucheron2, ... this process and the goal of Learning Theory is to formalize it. In this tutorial we consider a special case of the above process which is the ... yields maximum information. The identical distribution means that the obser-
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