Rule extraction from self-organizing networks
Autor(en): | Hammer, B Rechtien, A Strickert, M Villmann, T |
Herausgeber: | Dorronsoro, JR | Stichwörter: | ARTIFICIAL NEURAL NETWORKS; Computer Science; Computer Science, Artificial Intelligence | Erscheinungsdatum: | 2002 | Herausgeber: | SPRINGER-VERLAG BERLIN | Enthalten in: | ARTIFICIAL NEURAL NETWORKS - ICANN 2002 LECTURE NOTES IN COMPUTER SCIENCE |
Band: | 2415 | Startseite: | 877 | Seitenende: | 883 | Zusammenfassung: | Generalized relevance learning vector quantization (GRLVQ) [4] constitutes a prototype based clustering algorithm based on LVQ [5] with energy function and adaptive metric. We propose a method for extracting logical rules from a trained GRLVQ-network. Real valued attributes are automatically transformed to symbolic values. The rules are given in the form of a decision tree yielding several advantages: hybrid symbolic/subsymbolic descriptions can be obtained as an alternative and the complexity of the rules can be controlled. |
Beschreibung: | 12th International Conference on Artifical Neural Networks (ICANN 2002), MADRID, SPAIN, AUG 28-30, 2002 |
ISBN: | 9783540440741 | ISSN: | 03029743 |
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geprüft am 09.06.2024