Transcription of Introduction to Statistical Learning Theory
{{id}} {{{paragraph}}}
Example: quiz answers
Introduction to Statistical Learning Theory
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 is ,thedataconsistsof instance-label pairs,wherethelabel is either+1or 1.
Statistical Learning Theory 177 It turns out that there are many ways to do so, but no best one. For example in Physics, people tend to prefer models which have a small number of constants and that correspond to simple mathematical formulas. Often, the length of de-scription of a model in a coding language can be an indication of its complexity.
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: