000			03991nam a2200493 i 4500
001			6267226
003			IEEE
005			20220712204604.0
006			m o d
007			cr |n|||||||||
008			151223s2007 maua ob 001 eng d
020			_z9780262026253 _qprint
020			_a9780262255790 _qebook
020			_z0262255790 _qelectronic
035			_a(CaBNVSL)mat06267226
035			_a(IDAMS)0b000064818b41b8
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA76.9.D35 _bL38 2007eb
082	0	4	_a005.7/3 _222
245	0	0	_aLarge-scale kernel machines / _c[edited by] L�eon Bottou ... [et al.].
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc2007.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2007]
300			_a1 PDF (xii, 396 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aNeural information processing series
504			_aIncludes bibliographical references (p. [361]-387) and index.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aPervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale datasets, with detailed descriptions of algorithms and experiments carried out on realistically large datasets. At the same time it offers researchers information that can address the relative lack of theoretical grounding for many useful algorithms. After a detailed description of state-of-the-art support vector machine technology, an introduction of the essential concepts discussed in the volume, and a comparison of primal and dual optimization techniques, the book progresses from well-understood techniques to more novel and controversial approaches. Many contributors have made their code and data available online for further experimentation. Topics covered include fast implementations of known algorithms, approximations that are amenable to theoretical guarantees, and algorithms that perform well in practice but are difficult to analyze theoretically.ContributorsL�on Bottou, Yoshua Bengio, St�phane Canu, Eric Cosatto, Olivier Chapelle, Ronan Collobert, Dennis DeCoste, Ramani Duraiswami, Igor Durdanovic, Hans-Peter Graf, Arthur Gretton, Patrick Haffner, Stefanie Jegelka, Stephan Kanthak, S. Sathiya Keerthi, Yann LeCun, Chih-Jen Lin, Ga�l�le Loosli, Joaquin Qui�onero-Candela, Carl Edward Rasmussen, Gunnar R�tsch, Vikas Chandrakant Raykar, Konrad Rieck, Vikas Sindhwani, Fabian Sinz, S�ren Sonnenburg, Jason Weston, Christopher K. I. Williams, Elad Yom-TovL�on Bottou is a Research Scientist at NEC Labs America. Olivier Chapelle is with Yahoo! Research. He is editor of Semi-Supervised Learning (MIT Press, 2006). Dennis DeCoste is with Microsoft Research. Jason Weston is a Research Scientist at NEC Labs America.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aData structures (Computer science) _98188
650		0	_aMachine learning. _91831
655		0	_aElectronic books. _93294
700	1		_aBottou, L�eon. _921627
710	2		_aIEEE Xplore (Online Service), _edistributor. _921628
710	2		_aMIT Press, _epublisher. _921629
776	0	8	_iPrint version _z9780262026253
830		0	_aNeural information processing series _921630
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267226
942			_cEBK
999			_c72884 _d72884