Seminar on Computational Learning and
Adaptation
Law Discovery using Neural Networks
Kazumi Saito
NTT Communication Science Laboratories
2-4, Hikaridai, Seika-cho, Soraku-gun
Kyoto 619-0237 Japan
In this talk, I introduce RF6.2, a method for discovering empirical
laws from nominal and numeric data in the form of nominally
conditioned polynomials. A rule set of this type can be regarded as a
numeric function, and we show that such a function can be closely
approximated by a single three-layer neural network. RF6.2's basic
strategy involves training single neural networks using a
regularization method, then extracting a rule set through
decomposition of the best trained neural network with respect to the
nominal explanatory variables. Experiments using four data sets show
that RF6.2 works well in discovering very succinct and interesting
laws, even from data containing irrelevant variables and a small
amount of noise.
Date: Thurs., Feb. 1
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Time: 4:15-5:30PM
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Place: Cordura 100
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