000			03951nam a2200517 i 4500
001			6267512
003			IEEE
005			20190220121648.0
006			m o d
007			cr |n|||||||||
008			151228s1997 maua ob 001 eng d
010			_z 93034468 (print)
020			_a9780262291132 _qelectronic : v. 4
020			_z0262581264 _qv. l
020			_z9780262571180 _qprint
035			_a(CaBNVSL)mat06267512
035			_a(IDAMS)0b000064818b452e
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQ325.5 _b.C65 1994eb
082	0	0	_a006.3/1 _220
245	0	0	_aComputational learning theory and natural learning systems / _cedited by Stephen J. Hanson, George A. Drastal, and Ronald L. Rivest.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc1994-<c1997 >
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[1997]
300			_a1 PDF (v. <1-4 >) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
500			_a"A Bradford Book."
500			_aEditors vary.
504			_aIncludes bibliographical references and indexes.
505	1		_av. l. Constraints and prospects -- v. 2. Intersections between theory and experiment -- v. 3. Selecting good models -- v. 4. Making learning systems practical.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aThis is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and `Natural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew researchers from three historically distinct styles of learning research: computational learning theory, neural networks, and machine learning (a subfield of AI).Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems.Contributors : Klaus Abraham-Fuchs, Yasuhiro Akiba, Hussein Almuallim, Arunava Banerjee, Sanjay Bhansali, Alvis Brazma, Gustavo Deco, David Garvin, Zoubin Ghahramani, Mostefa Golea, Russell Greiner, Mehdi T. Harandi, John G. Harris, Haym Hirsh, Michael I. Jordan, Shigeo Kaneda, Marjorie Klenin, Pat Langley, Yong Liu, Patrick M. Murphy, Ralph Neuneier, E. M. Oblow, Dragan Obradovic, Michael J. Pazzani, Barak A. Pearlmutter, Nageswara S. V. Rao, Peter Rayner, Stephanie Sage, Martin F. Schlang, Bernd Schurmann, Dale Schuurmans, Leon Shklar, V. Sundareswaran, Geoffrey Towell, Johann Uebler, Lucia M. Vaina, Takefumi Yamazaki, Anthony M. Zador.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/28/2015.
650		0	_aComputational learning theory _xCongresses.
655		0	_aElectronic books.
700	1		_aHanson, Stephen Jos�e.
700	1		_aDrastal, George A.
700	1		_aRivest, Ronald L.
710	2		_aIEEE Xplore (Online Service), _edistributor.
710	2		_aMIT Press, _epublisher.
776	0	8	_iPrint version _z9780262571180
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267512
999			_c39424 _d39424