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• catalog abstract "This 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. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.".
• catalog contributor b11130024.
• catalog contributor b11130025.
• catalog created "c1999.".
• catalog date "1999".
• catalog date "c1999.".
• catalog dateCopyrighted "c1999.".
• catalog description "Feature extraction using an unsupervised neural network / Nathan Intrator -- Learning mixture models of spatial coherence / Suzanna Becker and Geoffrey E. Hinton -- Bayesian self-organization driven by prior probability distributions / Alan L. Yuille, Stelios M. Smirnakis, and Lei Xu -- Finding minimum entropy codes / H.B. Barlow, T.P. Kaushal, and G.J. Mitchison -- Learning population codes by minimizing description length / Richard S. Zemel and Geoffrey E. Hinton -- The Helmholz machine / Peter Dayan [and others] -- Factor analysis using delta-rule wake-sleep learning / Radford M. Neal and Peter Dayan -- Dimension reduction by local principal component analysis / Nandakishore Kambhatla and Todd K. Leen -- A resource-allocating network for function interpolation / John Platt -- Learning with preknowledge: clustering with point and graph matching distance measures / Steven Gold, Anand Rangarajan, and Eric Mjolsness -- ".
• catalog description "Includes bibliographical references and index.".
• catalog description "Learning to generalize from single examples in the dynamic link architecture / Wolfgang Konen and Christoph von der Malsburg.".
• catalog description "This 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. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.".
• catalog description "Unsupervised learning / H.B. Barlow -- Local synaptic learning rules suffice to maximize mutual information in a linear network / Ralph Linsker -- Convergent algorithm for sensory receptive field development / Joseph J. Atick and A. Norman Redlich -- Emergence of position-independent detectors of sense of rotation and dilation with Hebbian learning: an analysis / Kechen Zhang, Martin I. Sereno, and Margaret E.nSereno -- Learning invariance from transformation sequences / Peter Földiák -- Learning perceptually salient visual parameters using spatiotemporal smoothness constraints / James V. Stone -- What is the goal of sensory coding? / David J. Field -- An information-maximization approach to blind separation and blind deconvolution / Anthony J. Bell and Terrence J. Sejnowski -- Natural gradient works efficiently in learning / Shun-ichi Amari -- A fast fixed-point algorithm for independent component analysis / Aapo Hyvärinen and Erkki Oja -- ".
• catalog extent "xvi, 398 p. :".
• catalog identifier "026258168X (pbk. : alk. paper)".
• catalog isPartOf "Computational neuroscience".
• catalog issued "1999".
• catalog issued "c1999.".
• catalog language "eng".
• catalog publisher "Cambridge, Mass. : MIT Press,".
• catalog subject "612.8/2 21".
• catalog subject "Learning Computer simulation.".
• catalog subject "Learning Physiological aspects.".
• catalog subject "Neural computers.".
• catalog subject "Neural networks (Computer science)".
• catalog subject "Neural networks.".
• catalog subject "QP408 .U57 1999".
• catalog tableOfContents "Feature extraction using an unsupervised neural network / Nathan Intrator -- Learning mixture models of spatial coherence / Suzanna Becker and Geoffrey E. Hinton -- Bayesian self-organization driven by prior probability distributions / Alan L. Yuille, Stelios M. Smirnakis, and Lei Xu -- Finding minimum entropy codes / H.B. Barlow, T.P. Kaushal, and G.J. Mitchison -- Learning population codes by minimizing description length / Richard S. Zemel and Geoffrey E. Hinton -- The Helmholz machine / Peter Dayan [and others] -- Factor analysis using delta-rule wake-sleep learning / Radford M. Neal and Peter Dayan -- Dimension reduction by local principal component analysis / Nandakishore Kambhatla and Todd K. Leen -- A resource-allocating network for function interpolation / John Platt -- Learning with preknowledge: clustering with point and graph matching distance measures / Steven Gold, Anand Rangarajan, and Eric Mjolsness -- ".
• catalog tableOfContents "Learning to generalize from single examples in the dynamic link architecture / Wolfgang Konen and Christoph von der Malsburg.".
• catalog tableOfContents "Unsupervised learning / H.B. Barlow -- Local synaptic learning rules suffice to maximize mutual information in a linear network / Ralph Linsker -- Convergent algorithm for sensory receptive field development / Joseph J. Atick and A. Norman Redlich -- Emergence of position-independent detectors of sense of rotation and dilation with Hebbian learning: an analysis / Kechen Zhang, Martin I. Sereno, and Margaret E.nSereno -- Learning invariance from transformation sequences / Peter Földiák -- Learning perceptually salient visual parameters using spatiotemporal smoothness constraints / James V. Stone -- What is the goal of sensory coding? / David J. Field -- An information-maximization approach to blind separation and blind deconvolution / Anthony J. Bell and Terrence J. Sejnowski -- Natural gradient works efficiently in learning / Shun-ichi Amari -- A fast fixed-point algorithm for independent component analysis / Aapo Hyvärinen and Erkki Oja -- ".
• catalog title "Unsupervised learning : foundations of neural computation / edited by Geoffrey Hinton and Terrence J. Sejnowski.".
• catalog type "text".