LEADER 04147nam 2200685 450 001 9910260629203321 005 20200520144314.0 010 $a1-280-67835-6 010 $a9786613655288 010 $a0-262-30118-0 035 $a(CKB)2560000000082843 035 $a(OCoLC)794669892 035 $a(CaPaEBR)ebrary10569012 035 $a(SSID)ssj0000681124 035 $a(PQKBManifestationID)11390235 035 $a(PQKBTitleCode)TC0000681124 035 $a(PQKBWorkID)10655163 035 $a(PQKB)10302005 035 $a(MiAaPQ)EBC3339451 035 $a(CaBNVSL)mat06267536 035 $a(IDAMS)0b000064818b458c 035 $a(IEEE)6267536 035 $a(PPN)180003445 035 $a(Au-PeEL)EBL3339451 035 $a(CaPaEBR)ebr10569012 035 $a(CaONFJC)MIL365528 035 $a(EXLCZ)992560000000082843 100 $a20151223d2012 uy 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBoosting $efoundations and algorithms /$fRobert E. Schapire and Yoav Freund 210 1$aCambridge, Massachusetts :$cMIT Press,$dc2012. 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2012] 215 $a1 online resource (544 p.) 225 1 $aAdaptive computation and machine learning series 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-262-52603-4 311 $a0-262-01718-0 320 $aIncludes bibliographical references and indexes. 327 $aFoundations of machine learning -- Using AdaBoost to minimize training error -- Direct bounds on the generalization error -- The margins explanation for boosting's effectiveness -- Game theory, online learning, and boosting -- Loss minimization and generalizations of boosting -- Boosting, convex optimization, and information geometry -- Using confidence-rated weak predictions -- Multiclass classification problems -- Learning to rank -- Attaining the best possible accuracy -- Optimally efficient boosting -- Boosting in continuous time. 330 $aBoosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical.This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout. 410 0$aAdaptive computation and machine learning 606 $aBoosting (Algorithms) 606 $aSupervised learning (Machine learning) 608 $aElectronic books. 615 0$aBoosting (Algorithms) 615 0$aSupervised learning (Machine learning) 676 $a006.3/1 700 $aSchapire$b Robert E.$01052168 701 $aFreund$b Yoav$0999237 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910260629203321 996 $aBoosting$92483176 997 $aUNINA