A Median Variant of Generalized Learning Vector Quantization
Nebel D, Hammer B, Villmann T (2013)
In: Neural Information Processing. Lee M, Hirose A, Hou Z-G, Kil RM (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 19-26.
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| Veröffentlicht | Englisch
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Autor*in
Nebel, David;
Hammer, BarbaraUniBi 
;
Villmann, Thomas
;
Villmann, ThomasHerausgeber*in
Lee, Minho;
Hirose, Akira;
Hou, Zeng-Guang;
Kil, Rhee Man
Einrichtung
Abstract / Bemerkung
We introduce a median variant of the Generalized Learning Vector Quantization (GLVQ) algorithm. Thus, GLVQ can be used for classification problem learning, for which only dissimilarity information between the objects to be classified is available. For this purpose, the cost function of GLVQ is reformulated as a probabilistic model such that a generalized expectation maximization scheme can be applied as learning procedure. We give a rigorous mathematical proof for the new approach. Exemplary examples demonstrate the performance and the behavior of the algorithm.
Erscheinungsjahr
2013
Buchtitel
Neural Information Processing
Serientitel
Lecture Notes in Computer Science
Seite(n)
19-26
ISBN
978-3-642-42041-2
eISBN
978-3-642-42042-9
ISSN
0302-9743
eISSN
1611-3349
Page URI
https://pub.uni-bielefeld.de/record/2982101
Zitieren
Nebel D, Hammer B, Villmann T. A Median Variant of Generalized Learning Vector Quantization. In: Lee M, Hirose A, Hou Z-G, Kil RM, eds. Neural Information Processing. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2013: 19-26.
Nebel, D., Hammer, B., & Villmann, T. (2013). A Median Variant of Generalized Learning Vector Quantization. In M. Lee, A. Hirose, Z. - G. Hou, & R. M. Kil (Eds.), Lecture Notes in Computer Science. Neural Information Processing (pp. 19-26). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_3
Nebel, David, Hammer, Barbara, and Villmann, Thomas. 2013. “A Median Variant of Generalized Learning Vector Quantization”. In Neural Information Processing, ed. Minho Lee, Akira Hirose, Zeng-Guang Hou, and Rhee Man Kil, 19-26. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg.
Nebel, D., Hammer, B., and Villmann, T. (2013). “A Median Variant of Generalized Learning Vector Quantization” in Neural Information Processing, Lee, M., Hirose, A., Hou, Z. - G., and Kil, R. M. eds. Lecture Notes in Computer Science (Berlin, Heidelberg: Springer Berlin Heidelberg), 19-26.
Nebel, D., Hammer, B., & Villmann, T., 2013. A Median Variant of Generalized Learning Vector Quantization. In M. Lee, et al., eds. Neural Information Processing. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 19-26.
D. Nebel, B. Hammer, and T. Villmann, “A Median Variant of Generalized Learning Vector Quantization”, Neural Information Processing, M. Lee, et al., eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp.19-26.
Nebel, D., Hammer, B., Villmann, T.: A Median Variant of Generalized Learning Vector Quantization. In: Lee, M., Hirose, A., Hou, Z.-G., and Kil, R.M. (eds.) Neural Information Processing. Lecture Notes in Computer Science. p. 19-26. Springer Berlin Heidelberg, Berlin, Heidelberg (2013).
Nebel, David, Hammer, Barbara, and Villmann, Thomas. “A Median Variant of Generalized Learning Vector Quantization”. Neural Information Processing. Ed. Minho Lee, Akira Hirose, Zeng-Guang Hou, and Rhee Man Kil. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. Lecture Notes in Computer Science. 19-26.
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