Generalized relevance learning vector quantization
Hammer B, Villmann T (2002)
Neural Networks 15(8-9): 1059-1068.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Hammer, BarbaraUniBi 
;
Villmann, Thomas
;
Villmann, ThomasEinrichtung
Abstract / Bemerkung
We propose a new scheme for enlarging generalized learning vector quantization (GLVQ) with weighting factors for the input dimensions. The factors allow an appropriate scaling of the input dimensions according to their relevance. They are adapted automatically during training according to the specific classification task whereby training can be interpreted as stochastic gradient descent on an appropriate error function. This method leads to a more powerful classifier and to an adaptive metric with little extra cost compared to standard GLVQ. Moreover, the size of the weighting factors indicates the relevance of the input dimensions. This proposes a scheme for automatically pruning irrelevant input dimensions. The algorithm is verified on artificial data sets and the iris data from the UCI repository. Afterwards, the method is compared to several well known algorithms which determine the intrinsic data dimension on real world satellite image data.
Erscheinungsjahr
2002
Zeitschriftentitel
Neural Networks
Band
15
Ausgabe
8-9
Seite(n)
1059-1068
ISSN
08936080
Page URI
https://pub.uni-bielefeld.de/record/2982125
Zitieren
Hammer B, Villmann T. Generalized relevance learning vector quantization. Neural Networks. 2002;15(8-9):1059-1068.
Hammer, B., & Villmann, T. (2002). Generalized relevance learning vector quantization. Neural Networks, 15(8-9), 1059-1068. https://doi.org/10.1016/S0893-6080(02)00079-5
Hammer, Barbara, and Villmann, Thomas. 2002. “Generalized relevance learning vector quantization”. Neural Networks 15 (8-9): 1059-1068.
Hammer, B., and Villmann, T. (2002). Generalized relevance learning vector quantization. Neural Networks 15, 1059-1068.
Hammer, B., & Villmann, T., 2002. Generalized relevance learning vector quantization. Neural Networks, 15(8-9), p 1059-1068.
B. Hammer and T. Villmann, “Generalized relevance learning vector quantization”, Neural Networks, vol. 15, 2002, pp. 1059-1068.
Hammer, B., Villmann, T.: Generalized relevance learning vector quantization. Neural Networks. 15, 1059-1068 (2002).
Hammer, Barbara, and Villmann, Thomas. “Generalized relevance learning vector quantization”. Neural Networks 15.8-9 (2002): 1059-1068.
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