Multi-Channel Parallel Adaptation Theory for Rule Discovery

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dc.creatorFu, Li Min
dc.date2001-05-11
dc.date.accessioned2026-07-07T03:17:09Z
dc.date.available2026-07-07T03:17:09Z
dc.descriptionIn this paper, we introduce a new machine learning theory based on multi-channel parallel adaptation for rule discovery. This theory is distinguished from the familiar parallel-distributed adaptation theory of neural networks in terms of channel-based convergence to the target rules. We show how to realize this theory in a learning system named CFRule. CFRule is a parallel weight-based model, but it departs from traditional neural computing in that its internal knowledge is comprehensible. Furthermore, when the model converges upon training, each channel converges to a target rule. The model adaptation rule is derived by multi-level parallel weight optimization based on gradient descent. Since, however, gradient descent only guarantees local optimization, a multi-channel regression-based optimization strategy is developed to effectively deal with this problem. Formally, we prove that the CFRule model can explicitly and precisely encode any given rule set. Also, we prove a property related to asynchronous parallel convergence, which is a critical element of the multi-channel parallel adaptation theory for rule learning. Thanks to the quantizability nature of the CFRule model, rules can be extracted completely and soundly via a threshold-based mechanism. Finally, the practical application of the theory is demonstrated in DNA promoter recognition and hepatitis prognosis prediction.
dc.description21 pages, 1 figure, 7 tables
dc.identifierhttps://arxiv.org/abs/cs/0105022
dc.identifierhttp://arxiv.org/abs/cs/0105022
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30614
dc.subjectArtificial Intelligence
dc.subjectI2.6
dc.titleMulti-Channel Parallel Adaptation Theory for Rule Discovery
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