| Record ID | marc_columbia/Columbia-extract-20221130-004.mrc:512264056:3127 |
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LEADER: 03127mam a2200349 a 4500
001 1903743
005 20220609023321.0
008 960212s1996 njua b 001 0 eng
010 $a 96005256
020 $a0805812016 (acid-free paper)
035 $a(OCoLC)ocm34243608
035 $9ALZ6523CU
035 $a(NNC)1903743
035 $a1903743
040 $aDLC$cDLC$dOrLoB-B
050 00 $aQA76.87$b.M39 1996
082 00 $a006.3$220
245 00 $aMathematical perspectives on neural networks /$c[edited by] Paul Smolensky, Michael C. Mozer, David E. Rumelhart.
260 $aMahwah, N.J. :$bL. Erlbaum Associates,$c1996.
300 $axvi, 862 pages :$billustrations ;$c24 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
490 1 $aDevelopments in connectionist theory
504 $aIncludes bibliographical references and indexes.
505 00 $tPreface: Multilayer Structure of the Book and Its Summaries --$g1.$tOverview: Computational, Dynamical, and Statistical Perspectives on the Processing and Learning Problems in Neural Network Theory /$rPaul Smolensky --$g2.$tOverview: Computational Perspectives on Neural Networks /$rPaul Smolensky --$g3.$tComputation by Discrete Neural Nets /$rStan Franklin and Max Garzon --$g4.$tCircuit Complexity and Feedforward Neural Networks /$rIan Parberry --$g5.$tComplexity of Learning /$rJ. Stephen Judd --$g6.$tDeterministic and Randomized Local Search /$rEmile H. L. Aarts, Jan H. M. Korst and Patrick J. Zwietering --$g7.$tThe Mathematical Theory of the Analog Computer /$rMarian Boykan Pour-El --$g8.$tOverview: Dynamical Perspectives on Neural Networks /$rPaul Smolensky --$g9.$tDynamical Systems /$rMorris W. Hirsch --$g10.$tStatistical Analysis of Neural Networks /$rL. F. Abbott --$g11.$tNeural Networks in Control Systems /$rKumpati S. Narendra and Sai-Ming Li --
505 80 $g12.$tTime Series Analysis and Prediction /$rAndreas S. Weigend --$g13.$tOverview: Statistical Perspectives on Neural Networks /$rPaul Smolensky --$g14.$tRegularization in Neural Nets /$rRichard Szeliski --$g15.$tBackpropagation: The Basic Theory /$rDavid E. Rumelhart, Richard Durbin, Richard Golden and Yves Chauvin --$g16.$tInformation Theory and Neural Nets /$rJ. Rissanen --$g17.$tHidden Markov Models and Some Connections with Artificial Neural Nets /$rArthur Nadas and Robert L. Mercer --$g18.$tProbably Approximately Correct Learning and Decision-Theoretic Generalizations /$rDavid Haussler --$g19.$tParametric Statistical Estimation with Artificial Neural Networks /$rHalbert White --$g20.$tInductive Principles of Statistics and Learning Theory /$rV. N. Vapnik.
650 0 $aNeural networks (Computer science)$0http://id.loc.gov/authorities/subjects/sh90001937
700 1 $aSmolensky, Paul,$d1955-$0http://id.loc.gov/authorities/names/n83124453
700 1 $aMozer, Michael C.$0http://id.loc.gov/authorities/names/n90689733
700 1 $aRumelhart, David E.$0http://id.loc.gov/authorities/names/n85220013
830 0 $aDevelopments in connectionist theory.$0http://id.loc.gov/authorities/names/nr90022943
852 00 $boff,eng$hQA76.87$i.M39 1996