Connectionist symbol processing / edited by G.E. Hinton.

Format:

Book

Contributor:

Hinton, Geoffrey E.

Series:

Special issues of Artificial intelligence, an international journal

Language:

English

Subjects (All):

Connection machines.

Neural networks (Computer science).

Physical Description:

1 online resource (262 pages) : illustrations.

Edition:

First MIT Press edition.

Place of Publication:

Cambridge, Mass. : MIT Press, 1991.

System Details:

text file

Summary:

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.

Notes:

"A Bradford book."

"Reprinted from Artificial intelligence, an international journal, volume 46, numbers 1-2, 1990"--Title page verso.

OCLC-licensed vendor bibliographic record.

ISBN:

0262256363

9780262256360

OCLC:

827012532

Access Restriction:

Restricted for use by site license.