03460oam 2200685Ia 450 991078285700332120190503073347.00-262-29253-X1-282-24038-20-262-25510-39786612240386(CKB)1000000000721260(EBL)3338975(SSID)ssj0000135120(PQKBManifestationID)11954086(PQKBTitleCode)TC0000135120(PQKBWorkID)10062865(PQKB)10510313(StDuBDS)EDZ0000130720(OCoLC)310915974(CaBNVSL)mat06267199(IDAMS)0b000064818b4161(IEEE)6267199(OCoLC)310915974(OCoLC)316329315(OCoLC)609925477(OCoLC)628375064(OCoLC)646788347(OCoLC)663422124(OCoLC)743199120(OCoLC)764508014(OCoLC)816316194(OCoLC)966260623(OCoLC)967267169(OCoLC)988428383(OCoLC)991914433(OCoLC)1055360825(OCoLC)1066538357(OCoLC)1081202360(OCoLC-P)310915974(MaCbMITP)7921(Au-PeEL)EBL3338975(CaPaEBR)ebr10269466(CaONFJC)MIL224038(MiAaPQ)EBC3338975(PPN)170267822(EXLCZ)99100000000072126020090226d2009 uy 0engurcn|||||||||txtccrDataset shift in machine learning /[edited by] Joaquin Quiñonero-Candela [and others]Cambridge, Mass. MIT Press©20091 online resource (246 p.)Neural information processing seriesDescription based upon print version of record.0-262-17005-1 Includes bibliographical references and index.Contents; Series Foreword; Preface; I - Introduction to Dataset Shift; 1 - When Training and Test Sets Are Different: Characterizing Learning Transfer; 2 - Projection and Projectability; II - Theoretical Views on Dataset and Covariate Shift; 3 - Binary Classi cation under Sample Selection Bias; 4 - On Bayesian Transduction: Implications for the Covariate Shift Problem; 5 - On the Training/Test Distributions Gap: A Data Representation Learning Framework; III - Algorithms for Covariate Shift; 6 - Geometry of Covariate Shift with Applications to Active Learning7 - A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift 8 - Covariate Shift by Kernel Mean Matching; 9 - Discriminative Learning under Covariate Shift with a Single Optimization Problem; 10 - An Adversarial View of Covariate Shift and a Minimax Approach; IV - Discussion; 11 - Author Comments; References; Notation and Symbols; Contributors; IndexThis work is an overview of recent efforts in the machine learning community to deal with dataset and covariate shift which occurs when test and training inputs and outputs have different distributions.Neural information processing seriesMachine learningCOMPUTER SCIENCE/Machine Learning & Neural NetworksMachine learning.006.3/1Quiñonero-Candela Joaquin518515OCoLC-POCoLC-PBOOK9910782857003321Dataset shift in machine learning840582UNINA01402nam0 22003131i 450 UON0049274120231205105339.45195-19-97197-120190220d1986 |0itac50 bafreMULFI|||| |||||Actes du 9. Congres des Romanistes ScandinavesHelsinki 13-17 aout 1984edites par Elina Suomela-Harma et Olli ValikangasHelsinkiSociete Neophilologique1986438 p.22 cm.Dono Prof. Alberto Varvaro.IT-UONSI F. Varvaro0507001UON001740022001 Memoires de la Societè Neophilologique de Helsinki210 HelsinkiSocietè Neophilologique44Linguistica romanzaSaggiUONC070038FIFIHelsinkiUONL000062440.045LINGUE ROMANZE. GRAMMATICA22Suomela-HarmaElinaUONV241005ValikangasOlliUONV123057Societe NeophilologiqueUONV283354650ITSOL20250627RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00492741SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI F. Varvaro0507 SI 28022 7 Dono Prof. Alberto Varvaro.Actes du 9. Congres des Romanistes Scandinaves1547156UNIOR