Konferenzpaper
Autorenliste: Hammer, B; Rechtien, A; Strickert, M; Villmann, T
Jahr der Veröffentlichung: 2002
Seiten: 877-883
Zeitschrift: Lecture notes in computer science
Bandnummer: 2415
ISSN: 0302-9743
ISBN: 3-540-44074-7
DOI Link: https://doi.org/10.1007/3-540-46084-5_142
Konferenz: 12th International Conference on Artifical Neural Networks (ICANN 2002)
Verlag: Springer
Abstract:
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.
Zitierstile
Harvard-Zitierstil: Hammer, B., Rechtien, A., Strickert, M. and Villmann, T. (2002) Rule extraction from self-organizing networks, Lecture notes in computer science, 2415, pp. 877-883. https://doi.org/10.1007/3-540-46084-5_142
APA-Zitierstil: Hammer, B., Rechtien, A., Strickert, M., & Villmann, T. (2002). Rule extraction from self-organizing networks. Lecture notes in computer science. 2415, 877-883. https://doi.org/10.1007/3-540-46084-5_142
