(1991) Rule-Based Training of Neural Networks. Journal of Expert Systems with applications, 2 (1). pp. 47-58.

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Abstract

Rule-based expert systems either develop out of the direct involvement of a concerned expert or through the enormous efforts of intermediaries called knowledge engineers . In either case, knowledge engineering tools are inadequate in many ways to support the complex problem of expert system building. This article describes a set of experiments with adaptive neural networks which explore two types of learning, deductive and inductive, in the context of a rule-based, deterministic parser of Natural Language. Rule-based processing of Language is an important and complex domain. Experiences gained in this domain generalize to other rule-based domains. We report on those experiences and draw some general conclusions that are relevant to knowledge engineering activities and maintenance of rule-based systems.

Item Type:	Article
Subjects:	Computer
Department:	College of Computing and Mathematics > Information and Computer Science
Depositing User:	KANAAN ABED FAISAL
Date Deposited:	25 Jun 2008 12:00
Last Modified:	01 Nov 2019 14:03
URI:	http://eprints.kfupm.edu.sa/id/eprint/10654