MARC Record from marc_columbia

LEADER: 04645cam a2200481 a 4500
001 2349736
005 20220616025259.0
008 980224s1999 maua b 001 0 eng
010 $a 98014784
020 $a026258168X (pbk. : alk. paper)
035 $a(OCoLC)38550461
035 $a(OCoLC)ocm38550461
035 $9APM7572CU
035 $a(NNC)2349736
035 $a2349736
040 $aDLC$cDLC$dDLC$dNNC-M$dOrLoB-B
050 00 $aQP408$b.U57 1998
082 00 $a612.8/2$221
245 00 $aUnsupervised learning :$bfoundations of neural computation /$cedited by Geoffrey Hinton and Terrence J. Sejnowski.
260 $aCambridge, Mass. :$bMIT Press,$c1999.
300 $axvi, 398 pages :$billustrations ;$c23 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
490 1 $aComputational neuroscience
500 $a"A Bradford book."
504 $aIncludes bibliographical references and index.
505 00 $g1.$tUnsupervised Learning /$rH. B. Barlow --$g2.$tLocal Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network /$rRalph Linsker --$g3.$tConvergent Algorithm for Sensory Receptive Field Development /$rJoseph J. Atick and A. Norman Redlich --$g4.$tEmergence of Position-Independent Detectors of Sense of Rotation and Dilation with Hebbian Learning: An Analysis /$rKechen Zhang, Martin I. Sereno and Margaret E. Sereno --$g5.$tLearning Invariance from Transformation Sequences /$rPeter Foldiak --$g6.$tLearning Perceptually Salient Visual Parameters Using Spatiotemporal Smoothness Constraints /$rJames V. Stone --$g7.$tWhat Is the Goal of Sensory Coding? /$rDavid J. Field --$g8.$tAn Information-Maximization Approach to Blind Separation and Blind Deconvolution /$rAnthony J. Bell and Terrence J. Sejnowski --$g9.$tNatural Gradient Works Efficiently in Learning /$rShun-ichi Amari --$g10.$tA Fast Fixed-Point Algorithm for Independent Component Analysis /$rAapo Hyvdrinen and Erkki Oja --
505 80 $g11.$tFeature Extraction Using an Unsupervised Neural Network /$rNathan Intrator --$g12.$tLearning Mixture Models of Spatial Coherence /$rSuzanna Becker and Geoffrey E. Hinton --$g13.$tBayesian Self-Organization Driven by Prior Probability Distributions /$rAlan L. Yuille, Stelios M. Smirnakis and Lei Xu --$g14.$tFinding Minimum Entropy Codes /$rH. B. Barlow, T. P. Kaushal and G. J. Mitchison --$g15.$tLearning Population Codes by Minimizing Description Length /$rRichard S. Zemel and Geoffrey E. Hinton --$g16.$tThe Helmholtz Machine /$rPeter Dayan, Geoffrey E. Hinton and Radford M. Neal /$r[et al.] --$g17.$tFactor Analysis Using Delta-Rule Wake-Sleep Learning /$rRadford M. Neal and Peter Dayan --$g18.$tDimension Reduction by Local Principal Component Analysis /$rNandakishore Kambhatla and Todd K. Leen --$g19.$tA Resource-Allocating Network for Function Interpolation /$rJohn Platt --
505 80 $g20.$tLearning with Preknowledge: Clustering with Point and Graph Matching Distance Measures /$rSteven Gold, Anand Rangarajan and Eric Mjolsness --$g21.$tLearning to Generalize from Single Examples in the Dynamic Link Architecture /$rWolfgang Konen and Christoph von der Malsburg.
520 $aThis volume, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans.
520 8 $aThey are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.
650 0 $aLearning$xPhysiological aspects.$0http://id.loc.gov/authorities/subjects/sh85075523
650 0 $aNeural circuitry.$0http://id.loc.gov/authorities/subjects/sh85091089
650 0 $aLearning$xComputer simulation.
650 0 $aNeural computers.$0http://id.loc.gov/authorities/subjects/sh87008041
650 12 $aNerve Net.$0https://id.nlm.nih.gov/mesh/D009415
650 12 $aLearning.$0https://id.nlm.nih.gov/mesh/D007858
650 12 $aComputer Simulation.$0https://id.nlm.nih.gov/mesh/D003198
650 12 $aNeural Networks, Computer.$0https://id.nlm.nih.gov/mesh/D016571
700 1 $aHinton, Geoffrey E.$0http://id.loc.gov/authorities/names/n80165215
700 1 $aSejnowski, Terrence J.$q(Terrence Joseph)$0http://id.loc.gov/authorities/names/n86813480
830 0 $aComputational neuroscience.$0http://id.loc.gov/authorities/names/n86711840
852 00 $bsci$hQP408$i.U57 1999