LEADER 02927nam 2200721Ia 450 001 9910808092203321 005 20200520144314.0 010 $a9780429151095 010 $a0429151098 010 $a9781439830055 010 $a1439830053 024 7 $a10.1201/b12207 035 $a(CKB)2670000000210853 035 $a(EBL)952003 035 $a(OCoLC)798535723 035 $a(SSID)ssj0000677440 035 $a(PQKBManifestationID)11469824 035 $a(PQKBTitleCode)TC0000677440 035 $a(PQKBWorkID)10694638 035 $a(PQKB)11392930 035 $a(OCoLC)796675544 035 $a(Au-PeEL)EBL952003 035 $a(CaPaEBR)ebr10574375 035 $a(CaONFJC)MIL581225 035 $a(OCoLC)1350744941 035 $a(OCoLC-P)1350744941 035 $a(CaSebORM)9781439830055 035 $a(MiAaPQ)EBC952003 035 $a(OCoLC)1280138403 035 $a(FINmELB)ELB142738 035 $a(EXLCZ)992670000000210853 100 $a20120412d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aEnsemble methods $efoundations and algorithms /$fZhi-Hua Zhou 205 $a1st ed. 210 $aBoca Raton, FL $cTaylor & Francis$d2012 215 $a1 online resource (234 p.) 225 1 $aChapman & Hall/CRC machine learning & pattern recognition series 300 $aA Chapman & Hall book. 311 08$a9781439830031 311 08$a1439830037 320 $aIncludes bibliographical references and index. 327 $aFront Cover; Preface; Notations; Contents; 1. Introduction; 2. Boosting; 3. Bagging; 4. Combination Methods; 5. Diversity; 6. Ensemble Pruning; 7. Clustering Ensembles; 8. Advanced Topics; References 330 $aThis comprehensive book presents an in-depth and systematic introduction to ensemble methods for researchers in machine learning, data mining, and related areas. It helps readers solve modem problems in machine learning using these methods. The author covers the spectrum of research in ensemble methods, including such famous methods as boosting, bagging, and rainforest, along with current directions and methods not sufficiently addressed in other books. Chapters explore cutting-edge topics, such as semi-supervised ensembles, cluster ensembles, and comprehensibility, as well as successful applications--$cProvided by publisher. 410 0$aChapman & Hall/CRC machine learning & pattern recognition series. 606 $aMachine learning$xMathematics 606 $aAlgorithms 615 0$aMachine learning$xMathematics. 615 0$aAlgorithms. 676 $a006.3/1 686 $aBUS061000$aCOM021030$aCOM037000$2bisacsh 700 $aZhou$b Zhi-Hua$cPh. D.$0849299 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910808092203321 996 $aEnsemble methods$94118505 997 $aUNINA