000			04026nam a2200529 i 4500
001			6267536
003			IEEE
005			20190220121648.0
006			m o d
007			cr |n|||||||||
008			151223s2012 maua ob 001 eng d
010			_z 2011038972 (print)
020			_a9780262301183 _qelectronic book
020			_z9780262017183 _qhardcover : alk. paper
020			_z0262017180 _qhardcover : alk. paper
020			_z0262301180 _qelectronic book
035			_a(CaBNVSL)mat06267536
035			_a(IDAMS)0b000064818b458c
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQ325.75 _b.S33 2012eb
082	0	4	_a006.3/1 _223
100	1		_aSchapire, Robert E., _eauthor.
245	1	0	_aBoosting : _bfoundations and algorithms / _cRobert E. Schapire and Yoav Freund.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc2012.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2012]
300			_a1 PDF (xv, 526 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aAdaptive computation and machine learning series
504			_aIncludes bibliographical references and index.
505	0		_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.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_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.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aBoosting (Algorithms)
650		0	_aSupervised learning (Machine learning)
655		0	_aElectronic books.
700	1		_aFreund, Yoav.
710	2		_aIEEE Xplore (Online Service), _edistributor.
710	2		_aMIT Press, _epublisher.
776	0	8	_iPrint version _z9780262017183
830		0	_aAdaptive computation and machine learning
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267536
999			_c39447 _d39447