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Graphical models :foundations of neural computation

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"A Bradford book."Graphical models use graphs to represent and manipulate joint probability distributions. They have their roots in artificial intelligence, statistics, and neural networks. The clean mathematical formalism of the graphical models framework makes it possible to understand a wide variety of network-based approaches to computation, and in particular to understand many neural network algorithms and architectures as instances of a broader probabilistic methodology. It also makes it possible to identify novel features of neural network algorithms and architectures and to extend them to more general graphical models.This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithms and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research.Contributors H. Attias, C. M. Bishop, B. J. Frey, Z. Ghahramani, D. Heckerman, G. E. Hinton, R. Hofmann, R. A. Jacobs, Michael I. Jordan, H. J. Kappen, A. Krogh, R. Neal, S. K. Riis, F. B. Rodriguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss.OCLC-licensed vendor bibliographic record.

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Detail Information

Series Title

Computational Neuroscience Series

Call Number

-

Publisher

Cambridge, Mass. : : MIT Press,., 2001

Collation

1 online resource (xxiv, 421 pages) : illustrations

Language

English

ISBN/ISSN

0262291207

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

-

Subject(s)

NEURAL NETWORKS (COMPUTER SCIENCE)
Computer graphics.

Specific Detail Info

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Statement of Responsibility

edited by Michael I. Jordan and Terrence J. Sejnowski.

Other Information

Cataloger

-

Source

-

Validator

maya

Digital Object Identifier (DOI)

https://doi.org/10.7551/mitpress/3349.001.0001

Journal Volume

-

Journal Issue

-

Subtitle

-

Parallel Title

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