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003    OCoLC 
005    20200505023709.0 
006    m     o  d         
007    cr cnu---unuuu 
008    120604s2012    maua    ob    001 0 eng d 
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020    9780262301183|q(electronic bk.) 
020    0262301180|q(electronic bk.) 
020    |z9780262017183 
020    |z0262017180 
024 8  9786613655288 
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049    STJJ 
050  4 Q325.75|b.S33 2012eb 
072  7 COM|x005030|2bisacsh 
072  7 COM|x004000|2bisacsh 
082 04 006.3/1|223 
099    WORLD WIDE WEB|aE-BOOK|aMIT 
100 1  Schapire, Robert E. 
245 10 Boosting :|bfoundations and algorithms /|cRobert E. 
       Schapire and Yoav Freund. 
260    Cambridge, MA :|bMIT Press,|c©2012. 
300    1 online resource (xv, 526 pages) :|billustrations 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    data file|2rda 
490 1  Adaptive computation and machine learning 
504    Includes bibliographical references and index. 
505 0  Foundations 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. 
520    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. --|cEdited
       summary from book. 
588 0  Print version record. 
650  0 Boosting (Algorithms) 
650  0 Supervised learning (Machine learning) 
650  7 COMPUTERS|xEnterprise Applications|xBusiness Intelligence 
       Tools.|2bisacsh 
650  7 COMPUTERS|xIntelligence (AI) & Semantics.|2bisacsh 
650  7 Boosting (Algorithms)|2fast|0(OCoLC)fst01893023 
650  7 Supervised learning (Machine learning)|2fast
       |0(OCoLC)fst01139041 
655  4 Electronic books. 
700 1  Freund, Yoav. 
776 08 |iPrint version:|aSchapire, Robert E.|tBoosting.
       |dCambridge, MA : MIT Press, ©2012|z9780262017183|w(DLC)  
       2011038972|w(OCoLC)758388404 
830  0 Adaptive computation and machine learning. 
994    C0|bSTJ