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Connectionist Symbol Processing
     

Connectionist Symbol Processing

5.0 1
by Geoffrey Hinton
 

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

Overview

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.

Product Details

ISBN-13:
9780262581066
Publisher:
MIT Press
Publication date:
10/17/1991
Series:
Special Issues of Artificial Intelligence
Edition description:
1st MIT Press ed
Pages:
268
Product dimensions:
7.00(w) x 10.00(h) x 0.50(d)
Age Range:
18 Years

Meet the Author

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

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Connectionist Symbol Processing 5 out of 5 based on 0 ratings. 1 reviews.
Anonymous More than 1 year ago