Transcription of Introduction to Statistical Learning Theory
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Introductionto StatisticalLearningTheoryOlivierBousquet 1, St ephaneBoucheron2, andG aborLugosi31 Max-Planck ,D-72076T e deParis-Sud,Laboratoired'InformatiqueB^a timent 490,F-91405 Orsay 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:176 Bousquet,Boucheron& a predictionsusingthismodelOfcourse,thisde nitionis verygeneralandcouldbe toactuallyautomatethisprocessandthegoalo fLearningTheoryis considera specialcaseoftheabove processwhich i
The main goal of statistical learning theory is to provide a framework for study-ing the problem of inference, that is of gaining knowledge, making predictions, making decisions or constructing models from a set of data. This is studied in a statistical framework, that is there are assumptions of statistical nature about
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