Integrating Neural and Symbolic Approaches: A Symbolic Learning Scheme for a Connectionist Associative Memory
作者:
JOERGP. UEBERLA,
ARUN JAGOTA,
期刊:
Connection Science
(Taylor Available online 1993)
卷期:
Volume 5,
issue 3-4
页码: 377-393
ISSN:0954-0091
年代: 1993
DOI:10.1080/09540099308915706
出版商: Taylor & Francis Group
关键词: Associative memory;neural networks;symbolic representation
数据来源: Taylor
摘要:
This paper deals with the integration of neural and symbolic approaches. It focuses on associative memories where a connectionist architecture tries to provide a storage and retrieval component for the symbolic level. In this light, the classic model for associative memory, the Hopfield network is briefly reviewed. Then, a new model for associative memory, the hybrid Hopfield-clique network is presented in detail. Its application to a typically symbolic task, the post -processing of the output of an optical character recognizer, is also described. In the author's view, the hybrid Hopfield -clique network constitutes an example of a successful integration of the two approaches. It uses a symbolic learning scheme to train a connectionist network, and through this integration, it can provide perfect storage and recall. As a conclusion, an analysis of what can be learned from this specific architecture is attempted. In the case of this model, a guarantee for perfect storage and recall can only be given because it was possible to analyze the problem using the well-defined symbolic formalism of graph theory. In general, we think that finding an adequate formalism for a given problem is an important step towards solving it.
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