03722nam 2200793Ia 450 991095480390332120200520144314.09780262292535026229253X97812822403841282240382978026225510302622551039786612240386(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(PPN)170267822(FR-PaCSA)88800221(MiAaPQ)EBC3338975(FRCYB88800221)88800221(EXLCZ)99100000000072126020140717d2009 uy 0engurcn|||||||||txtccrDataset shift in machine learning /[edited by] Joaquin Quinonero-Candela ... [et al.]1st ed.Cambridge, Mass. MIT Pressc20091 online resource (246 p.)Neural information processing seriesDescription based upon print version of record.9780262170055 0262170051 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 series.Machine learningMachine learningMathematical modelsMachine learning.Machine learningMathematical models.006.3/1Quinonero-Candela Joaquin518515MiAaPQMiAaPQMiAaPQBOOK9910954803903321Dataset shift in machine learning840582UNINA