Coverart for item
The Resource Mathematical perspectives on neural networks, [edited by] Paul Smolensky, Michael C. Mozer, David E. Rumelhart

Mathematical perspectives on neural networks, [edited by] Paul Smolensky, Michael C. Mozer, David E. Rumelhart

Label
Mathematical perspectives on neural networks
Title
Mathematical perspectives on neural networks
Statement of responsibility
[edited by] Paul Smolensky, Michael C. Mozer, David E. Rumelhart
Contributor
Subject
Language
eng
Member of
Cataloging source
DLC
Illustrations
illustrations
Index
index present
LC call number
QA76.87
LC item number
.M39 1996
Literary form
non fiction
Nature of contents
bibliography
Series statement
Developments in connectionist theory
Mathematical perspectives on neural networks, [edited by] Paul Smolensky, Michael C. Mozer, David E. Rumelhart
Label
Mathematical perspectives on neural networks, [edited by] Paul Smolensky, Michael C. Mozer, David E. Rumelhart
Publication
Related Contributor
Related Location
Related Agents
Related Authorities
Related Subjects
Related Items
Bibliography note
Includes bibliographical references and indexes
Contents
  • Complexity of learning
  • J. Stephen Judd
  • Deterministic and randomized local search
  • Emile H. L. Aarts, Jan H. M. Korst, and Patrick J. Zwietering
  • Mathematical theory of the analog computer
  • Marian B. Pour-El
  • Overview : dynamical perspectives on neural networks
  • Paul Smolensky
  • Dynamical systems
  • Morris W. Hirsch
  • Overview : computational, dynamical, and statistical perspectives on the processing and learning problems in neural network theory
  • Statistical analysis of neural networks
  • L. F. Abbott
  • Neural networks in control systems
  • Kumpati S. Narendra and Sai-Ming Li
  • Time series analysis and prediction
  • Andreas S. Weigend
  • Overview : statistical perspectives on neural networks
  • Paul Smolensky
  • Regularization in neural nets
  • Richard Szeliski --
  • Paul Smolensky
  • Overview : computational perspectives on neural networks
  • Paul Smolensky
  • Computation by discrete neural nets
  • Stan Franklin and Max Garzon
  • Circuit complexity and feedforward neural networks
  • Ian Parberry
  • Parametric statistical estimation with artificial neural networks
  • Halbert White
  • Inductive principles of statistics and learning theory
  • V. N. Vapnik
  • Backpropagation : the basic theory
  • David E. Rumelhart, Richard Durbin, Richard Golden, and Yves Chauvin
  • Information theory and neural nets
  • J. Rissanen
  • Hidden Markov models and some connections with artificial neural nets
  • Arthur Nádas and Robert L. Mercer
  • Probably approximately correct learning and decision-theoretic generalizations
  • David Haussler
http://library.link/vocab/cover_art
https://contentcafe2.btol.com/ContentCafe/Jacket.aspx?Return=1&Type=S&Value=9780805812015&userID=ebsco-test&password=ebsco-test
Dimensions
24 cm.
http://library.link/vocab/discovery_link
{'f': 'http://opac.lib.rpi.edu/record=b1334076'}
Extent
xvi, 862 p.
Isbn
9780805812015
Isbn Type
(acid-free paper)
Lccn
96005256
Other physical details
ill.

Library Locations

    • Folsom LibraryBorrow it
      110 8th St, Troy, NY, 12180, US
      42.729766 -73.682577
Processing Feedback ...