LEADER 03722nam 2200793Ia 450 001 9910954803903321 005 20200520144314.0 010 $a9780262292535 010 $a026229253X 010 $a9781282240384 010 $a1282240382 010 $a9780262255103 010 $a0262255103 024 8 $a9786612240386 035 $a(CKB)1000000000721260 035 $a(EBL)3338975 035 $a(SSID)ssj0000135120 035 $a(PQKBManifestationID)11954086 035 $a(PQKBTitleCode)TC0000135120 035 $a(PQKBWorkID)10062865 035 $a(PQKB)10510313 035 $a(StDuBDS)EDZ0000130720 035 $a(OCoLC)310915974 035 $a(CaBNVSL)mat06267199 035 $a(IDAMS)0b000064818b4161 035 $a(IEEE)6267199 035 $a(OCoLC)310915974$z(OCoLC)316329315$z(OCoLC)609925477$z(OCoLC)628375064$z(OCoLC)646788347$z(OCoLC)663422124$z(OCoLC)743199120$z(OCoLC)764508014$z(OCoLC)816316194$z(OCoLC)966260623$z(OCoLC)967267169$z(OCoLC)988428383$z(OCoLC)991914433$z(OCoLC)1055360825$z(OCoLC)1066538357$z(OCoLC)1081202360 035 $a(OCoLC-P)310915974 035 $a(MaCbMITP)7921 035 $a(Au-PeEL)EBL3338975 035 $a(CaPaEBR)ebr10269466 035 $a(CaONFJC)MIL224038 035 $a(PPN)170267822 035 $a(FR-PaCSA)88800221 035 $a(MiAaPQ)EBC3338975 035 $a(FRCYB88800221)88800221 035 $a(EXLCZ)991000000000721260 100 $a20140717d2009 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aDataset shift in machine learning /$f[edited by] Joaquin Quinonero-Candela ... [et al.] 205 $a1st ed. 210 $aCambridge, Mass. $cMIT Press$dc2009 215 $a1 online resource (246 p.) 225 1 $aNeural information processing series 300 $aDescription based upon print version of record. 311 08$a9780262170055 311 08$a0262170051 320 $aIncludes bibliographical references and index. 327 $aContents; 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 Learning 327 $a7 - 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; Index 330 8 $aThis work is an overview of recent efforts in the machine learning community to deal with dataset and covariate shift which occurs when test and training inputs and outputs have different distributions. 410 0$aNeural information processing series. 606 $aMachine learning 606 $aMachine learning$xMathematical models 615 0$aMachine learning. 615 0$aMachine learning$xMathematical models. 676 $a006.3/1 701 $aQuinonero-Candela$b Joaquin$0518515 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910954803903321 996 $aDataset shift in machine learning$9840582 997 $aUNINA