Comparison of relevance learning vector quantization with other metric adaptive classification methods

Villmann T, Schleif F-M, Hammer B (2006)
Neural Networks 19(5): 610-622.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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DOI
Autor*in
Villmann, T.; Schleif, Frank-MichaelUniBi ; Hammer, BarbaraUniBi
Einrichtung
Technische Fakultät > AG Machine Learning
Erscheinungsjahr
2006
Zeitschriftentitel
Neural Networks
Band
19
Ausgabe
5
Seite(n)
610-622
ISSN
0893-6080
Page URI
https://pub.uni-bielefeld.de/record/1994237

Zitieren

Villmann T, Schleif F-M, Hammer B. Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks. 2006;19(5):610-622.
Villmann, T., Schleif, F. - M., & Hammer, B. (2006). Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks, 19(5), 610-622. https://doi.org/10.1016/j.neunet.2005.07.013
Villmann, T., Schleif, Frank-Michael, and Hammer, Barbara. 2006. “Comparison of relevance learning vector quantization with other metric adaptive classification methods”. Neural Networks 19 (5): 610-622.
Villmann, T., Schleif, F. - M., and Hammer, B. (2006). Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks 19, 610-622.
Villmann, T., Schleif, F.-M., & Hammer, B., 2006. Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks, 19(5), p 610-622.
T. Villmann, F.-M. Schleif, and B. Hammer, “Comparison of relevance learning vector quantization with other metric adaptive classification methods”, Neural Networks, vol. 19, 2006, pp. 610-622.
Villmann, T., Schleif, F.-M., Hammer, B.: Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks. 19, 610-622 (2006).
Villmann, T., Schleif, Frank-Michael, and Hammer, Barbara. “Comparison of relevance learning vector quantization with other metric adaptive classification methods”. Neural Networks 19.5 (2006): 610-622.

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