000			03817nam a2200553 i 4500
001			6267404
003			IEEE
005			20220712204655.0
006			m o d
007			cr |n|||||||||
008			151223s1990 maua ob 001 eng d
020			_a9780262276559 _qebook
020			_z0585359342 _qelectronic
020			_z9780585359342 _qelectronic
020			_z0262276550 _qelectronic
020			_z9780262519243 _qprint
035			_a(CaBNVSL)mat06267404
035			_a(IDAMS)0b000064818b43f0
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA76.5 _b.J83 1990eb
082	0		_a006.3 _220
100	1		_aJudd, J. Stephen, _eauthor. _922630
245	1	0	_aNeural network design and the complexity of learning / _cJ. Stephen Judd.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc1990.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[1990]
300			_a1 PDF (150 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aNeural network modeling and connectionism
500			_a"A Bradford book."
504			_aIncludes bibliographical references (p. [137]-143) and index.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aUsing the tools of complexity theory, Stephen Judd develops a formal description of associative learning in connectionist networks. He rigorously exposes the computational difficulties in training neural networks and explores how certain design principles will or will not make the problems easier.Judd looks beyond the scope of any one particular learning rule, at a level above the details of neurons. There he finds new issues that arise when great numbers of neurons are employed and he offers fresh insights into design principles that could guide the construction of artificial and biological neural networks.The first part of the book describes the motivations and goals of the study and relates them to current scientific theory. It provides an overview of the major ideas, formulates the general learning problem with an eye to the computational complexity of the task, reviews current theory on learning, relates the book's model of learning to other models outside the connectionist paradigm, and sets out to examine scale-up issues in connectionist learning.Later chapters prove the intractability of the general case of memorizing in networks, elaborate on implications of this intractability and point out several corollaries applying to various special subcases. Judd refines the distinctive characteristics of the difficulties with families of shallow networks, addresses concerns about the ability of neural networks to generalize, and summarizes the results, implications, and possible extensions of the work.J. Stephen Judd is Visiting Assistant Professor of Computer Science at The California Institute of Technology. Neural Network Design and the Complexity of Learning is included in the Network Modeling and Connectionism series edited by Jeffrey Elman.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aArtificial intelligence. _93407
650		0	_aComputational complexity. _93729
650		0	_aNeural computers. _94963
655		0	_aElectronic books. _93294
710	2		_aIEEE Xplore (Online Service), _edistributor. _922631
710	2		_aMIT Press, _epublisher. _922632
710	2		_aNetLibrary, Inc. _922633
776	0	8	_iPrint version _z9780262519243
830		0	_aNeural network modeling and connectionism _922449
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267404
942			_cEBK
999			_c73058 _d73058