Presentation 1997/3/17
Rule Extraction from Data with Continuous Valued Inputs and Discrete Valued Outputs using Neural Networks
Hiroki Ueda, Masumi Ishikawa,
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Abstract(in English) The paper focuses on rule extraction from data with continuous valued inputs and discrete valued-outputs. Considering the understandability of resulting rules, it is assumed that simple rules with large generalization ability are desirable. In order to obtain them, an information criterion representing this trade-off is used for model selection from among a set of networks with various complexities.
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Keyword(in English) rule extraction / structural learning / information criterion / continuous valued inputs
Paper # NC96-121
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Committee NC
Conference Date 1997/3/17(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Rule Extraction from Data with Continuous Valued Inputs and Discrete Valued Outputs using Neural Networks
Sub Title (in English)
Keyword(1) rule extraction
Keyword(2) structural learning
Keyword(3) information criterion
Keyword(4) continuous valued inputs
1st Author's Name Hiroki Ueda
1st Author's Affiliation Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology()
2nd Author's Name Masumi Ishikawa
2nd Author's Affiliation Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology
Date 1997/3/17
Paper # NC96-121
Volume (vol) vol.96
Number (no) 583
Page pp.pp.-
#Pages 8
Date of Issue