Faisal, Kanaan A (1991) Rule-Based Training of Neural Networks. Journal of Expert Systems with applications, 2 (1). pp. 47-58.
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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.
|Divisions:||College Of Computer Sciences and Engineering > Information and Computer Science Dept|
|Creators:||Faisal, Kanaan A|
|Deposited By:||KANAAN ABED FAISAL|
|Deposited On:||25 Jun 2008 15:00|
|Last Modified:||12 Apr 2011 13:15|
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