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
Journal: ARTIFICIAL NEURAL NETWORKS - ICANN 2002
LECTURE NOTES IN COMPUTER SCIENCE
Volumen: 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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