Connectionist Symbol Processing

Geoffrey Hinton

ISBN 10: 026258106X / ISBN 13: 9780262581066

Published by MIT Press, 1991

About this Item

Connectionist Symbol Processing | Geoffrey Hinton | Taschenbuch | Kartoniert / Broschiert | Englisch | 1991 | MIT Press | EAN 9780262581066 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 120963617

Bibliographic Details

• Title: Connectionist Symbol Processing
• Publisher: MIT Press
• Publication Date: 1991
• Language: English
• ISBN 10: 026258106X
• ISBN 13: 9780262581066
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 280W x 216H x 15D millimeters 280 millimeters width by 216 millimeters height by 15 millimeters depth
• Weight: 688 grams
• Seller catalogs:
  • Bücher

About this title

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.

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.