Automated Knowledge Acquisition

Front Cover
Prentice Hall, 1994 - Computers - 378 pages
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This tutorial provides clear explanations of techniques for automated knowledge acquisition. Covers topics such as induction algorithms using decision trees; induction algorithms using progressive rule generation; sub-symbolic learning methods, artificial neural networks; other machine learning paradigms; theoretical considerations; the extraction of rules and concepts using a single-layered Hebbian neural network; BRAINNE - automated knowledge acquisition using multi-layered neural network; and BRAINNE in the real world. For computer professionals who wish to gain a good understanding of automated knowledge acquisition techniques.

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Induction algorithms using decision trees
Induction algorithms using progressive
Subsymbolic learning methods artificial neural

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