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Abstract: This thesis describes the architecture of learning systems which can explain their decisions through a rule-based knowledge representation. Two problems in learning are addressed: pattern classification and function approximation. In Part I, a pattern classifier for discrete-valued problems is presented. The system utilizes an information-theoretic algorithm for constructing informative rules from example data. These rules are then used to construct a computational network to perform parallel... (Update)
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BibTeX entry: (Update)
@misc{ higgins-classification,
author = "Charles M. Higgins and Jr.",
title = "Classification and Approximation with Rule-Based Networks",
url = "citeseer.ist.psu.edu/179666.html" }
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Documents on the same site (http://www.klab.caltech.edu/~chuck/misc_html/rule_based.html): More
Learning Fuzzy Rule-Based Neural Networks for Function.. - Higgins, Goodman (Correct)
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