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Connectionist Symbol Processing

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Connectionist Symbol Processing Cover

 

Synopses & Reviews

Publisher Comments:

The six contributions in Connectionist Symbol Processing address the current tension within the artificial intelligence community between advocates of powerful symbolic representations that lack efficient learning procedures and advocates of relatively simple learning procedures that lack the ability to represent complex structures effectively. The authors seek to extend the representational power of connectionist networks without abandoning the automatic learning that makes these networks interesting.

Aware of the huge gap that needs to be bridged, the authors intend their contributions to be viewed as exploratory steps in the direction of greater representational power for neural networks. If successful, this research could make it possible to combine robust general purpose learning procedures and inherent representations of artificial intelligence—a synthesis that could lead to new insights into both representation and learning.

Contents: Preface, G. E. Hinton. BoltzCONS: Dynamic Symbol Structures in a Connectionist Network, D. S. Touretzky. Mapping Part-Whole Hierarchies into Connectionist Networks, G. E. Hinton. Recursive Distributed Representations, J. B. Pollack. Mundane Reasoning by Settling on a Plausible Model, M. Derthick. Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems, P. Smolensky. Learning and Applying Contextual Constraints in Sentence Comprehension, M. F. St. John and J. L. McClelland.

Synopsis:

Addressing the current tension within the artificial intelligence community between advocates of powerful symbolic representations that lack efficient learning procedures and advocates of relatively simple learning procedures that lack the ability to represent complex structures effectively.

Synopsis:

The six contributions in Connectionist Symbol Processing address the current tension within the artificial intelligence community between advocates of powerful symbolic representations that lack efficient learning procedures and advocates of relatively simple learning procedures that lack the ability to represent complex structures effectively. The authors seek to extend the representational power of connectionist networks without abandoning the automatic learning that makes these networks interesting.Aware of the huge gap that needs to be bridged, the authors intend their contributions to be viewed as exploratory steps in the direction of greater representational power for neural networks. If successful, this research could make it possible to combine robust general purpose learning procedures and inherent representations of artificial intelligence — a synthesis that could lead to new insights into both representation and learning.

About the Author

Geoffrey Hinton is Professor of Computer Science at the University of Toronto.

Product Details

ISBN:
9780262581066
Author:
Hinton, Geoffrey
Publisher:
Bradford Book
Location:
Cambridge, Mass. :
Subject:
Neural Networks
Subject:
Neural networks (computer science)
Subject:
Connection machines.
Subject:
Cognitive Psychology
Subject:
Computers-Reference - General
Copyright:
Edition Number:
1st MIT Press ed.
Edition Description:
Trade paper
Series:
Special Issues of < I > Artificial Intelligence < /I > Connectionist Symbol Processing
Series Volume:
1273-F
Publication Date:
19911031
Binding:
TRADE PAPER
Grade Level:
from 17
Language:
English
Illustrations:
Yes
Pages:
268
Dimensions:
10 x 7 in

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Health and Self-Help » Psychology » Cognitive Science
Humanities » Philosophy » General

Connectionist Symbol Processing Used Trade Paper
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$9.95 In Stock
Product details 268 pages M I T Press - English 9780262581066 Reviews:
"Synopsis" by , Addressing the current tension within the artificial intelligence community between advocates of powerful symbolic representations that lack efficient learning procedures and advocates of relatively simple learning procedures that lack the ability to represent complex structures effectively.
"Synopsis" by , The six contributions in Connectionist Symbol Processing address the current tension within the artificial intelligence community between advocates of powerful symbolic representations that lack efficient learning procedures and advocates of relatively simple learning procedures that lack the ability to represent complex structures effectively. The authors seek to extend the representational power of connectionist networks without abandoning the automatic learning that makes these networks interesting.Aware of the huge gap that needs to be bridged, the authors intend their contributions to be viewed as exploratory steps in the direction of greater representational power for neural networks. If successful, this research could make it possible to combine robust general purpose learning procedures and inherent representations of artificial intelligence — a synthesis that could lead to new insights into both representation and learning.
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