Introduction to semi-supervised learning by WISC xiaojin ZHU

Introduction to semi-supervised learning by WISC xiaojin ZHU

ID:39786158

大小:1.15 MB

页数:130页

时间:2019-07-11

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1、IntroductiontoSemi-SupervisedLearningSynthesisLecturesonArtificialIntelligenceandMachineLearningEditorsRonaldJ.Brachman,Yahoo!ResearchThomasDietterich,OregonStateUniversityIntroductiontoSemi-SupervisedLearningXiaojinZhuandAndrewB.Goldberg2009ActionProgrammingLanguagesMichaelThielscher2008Represent

2、ationDiscoveryusingHarmonicAnalysisSridharMahadevan2008EssentialsofGameTheory:AConciseMultidisciplinaryIntroductionKevinLeyton-Brown,YoavShoham2008AConciseIntroductiontoMultiagentSystemsandDistributedArtificialIntelligenceNikosVlassis2007IntelligentAutonomousRobotics:ARobotSoccerCaseStudyPeterSton

3、e2007Copyright©2009byMorgan&ClaypoolAllrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmittedinanyformorbyanymeanselectronic,mechanical,photocopy,recording,oranyotherexceptforbriefquotationsinprintedreviews,withoutthepriorpermissionofthepublisher.Introduction

4、toSemi-SupervisedLearningXiaojinZhuandAndrewB.Goldbergwww.morganclaypool.comISBN:9781598295474paperbackISBN:9781598295481ebookDOI10.2200/S00196ED1V01Y200906AIM006APublicationintheMorgan&ClaypoolPublishersseriesSYNTHESISLECTURESONARTIFICIALINTELLIGENCEANDMACHINELEARNINGLecture#6SeriesEditors:Ronal

5、dJ.Brachman,Yahoo!ResearchThomasDietterich,OregonStateUniversitySeriesISSNSynthesisLecturesonArtificialIntelligenceandMachineLearningPrint1939-4608Electronic1939-4616IntroductiontoSemi-SupervisedLearningXiaojinZhuandAndrewB.GoldbergUniversityofWisconsin,MadisonSYNTHESISLECTURESONARTIFICIALINTELLIG

6、ENCEANDMACHINELEARNING#6MMorgan&cLaypoolpublishers&CABSTRACTSemi-supervisedlearningisalearningparadigmconcernedwiththestudyofhowcomputersandnaturalsystemssuchashumanslearninthepresenceofbothlabeledandunlabeleddata.Traditionally,learninghasbeenstudiedeitherintheunsupervisedparadigm(e.g.,clustering

7、,outlierdetection)whereallthedataisunlabeled,orinthesupervisedparadigm(e.g.,classification,regression)whereallthedataislabeled.Thegoalofsemi-supervisedlearningistounderstandhowcombininglabeledandunlabeleddatamaychangeth

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