000 | 03817nam a2200553 i 4500 | ||
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001 | 6267404 | ||
003 | IEEE | ||
005 | 20220712204655.0 | ||
006 | m o d | ||
007 | cr |n||||||||| | ||
008 | 151223s1990 maua ob 001 eng d | ||
020 |
_a9780262276559 _qebook |
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020 |
_z0585359342 _qelectronic |
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020 |
_z9780585359342 _qelectronic |
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020 |
_z0262276550 _qelectronic |
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020 |
_z9780262519243 _qprint |
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035 | _a(CaBNVSL)mat06267404 | ||
035 | _a(IDAMS)0b000064818b43f0 | ||
040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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050 | 4 |
_aQA76.5 _b.J83 1990eb |
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082 | 0 |
_a006.3 _220 |
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100 | 1 |
_aJudd, J. Stephen, _eauthor. _922630 |
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245 | 1 | 0 |
_aNeural network design and the complexity of learning / _cJ. Stephen Judd. |
264 | 1 |
_aCambridge, Massachusetts : _bMIT Press, _cc1990. |
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264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[1990] |
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300 |
_a1 PDF (150 pages) : _billustrations. |
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336 |
_atext _2rdacontent |
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337 |
_aelectronic _2isbdmedia |
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338 |
_aonline resource _2rdacarrier |
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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 |
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650 | 0 |
_aComputational complexity. _93729 |
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650 | 0 |
_aNeural computers. _94963 |
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655 | 0 |
_aElectronic books. _93294 |
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710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. _922631 |
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710 | 2 |
_aMIT Press, _epublisher. _922632 |
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710 | 2 |
_aNetLibrary, Inc. _922633 |
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776 | 0 | 8 |
_iPrint version _z9780262519243 |
830 | 0 |
_aNeural network modeling and connectionism _922449 |
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856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267404 |
942 | _cEBK | ||
999 |
_c73058 _d73058 |