| 刊年 | 1991 |
| G/SMD | リモートファイル |
| 形態 | 1 online resource (262 p.) : ill. |
| シリーズ名 | Special issues of Artificial intelligence, an international journal
|
| 注記 | "A Bradford book."
"Reprinted from Artificial intelligence, an international journal, volume 46, numbers 1-2, 1990"--T.p. verso
Includes bibliographical references and index
Restricted to subscribers or individual electronic text purchasers
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
Also available in print
Mode of access: World Wide Web
Description based on PDF viewed 12/23/2015
URL:https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267281(Abstract with links to resource) |
| 出版国 | アメリカ合衆国 |
| 標題言語 | 英語 |
| 本文言語 | 英語 |
| 著者情報 | Hinton, Geoffrey E
|
| ISBN | 9780262256360(: electronic bk)
|
| 無効/取消ISBN | 9780262581066(: electronic bk)
|
| 件名 | LCSH:Connectionmachines
LCSH:Neuralnetworks(Computerscience)
|
| NCID | 6267281 |
| IDENT | https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267281 |