Relational Extensions of Learning Vector Quantization
Hammer B, Schleif F-M, Zhu X (2011)
In: Neural Information Processing. Lu B-L, Zhang L, Kwok J (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 481-489.
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| Veröffentlicht | Englisch
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Herausgeber*in
Lu, Bao-Liang;
Zhang, Liqing;
Kwok, James
Abstract / Bemerkung
Prototype-based models offer an intuitive interface to given data sets by means of an inspection of the model prototypes. Supervised classification can be achieved by popular techniques such as learning vector quantization (LVQ) and extensions derived from cost functions such as generalized LVQ (GLVQ) and robust soft LVQ (RSLVQ). These methods, however, are restricted to Euclidean vectors and they cannot be used if data are characterized by a general dissimilarity matrix. In this approach, we propose relational extensions of GLVQ and RSLVQ which can directly be applied to general possibly non-Euclidean data sets characterized by a symmetric dissimilarity matrix.
Erscheinungsjahr
2011
Buchtitel
Neural Information Processing
Serientitel
Lecture Notes in Computer Science
Seite(n)
481-489
ISBN
978-3-642-24957-0
eISBN
978-3-642-24958-7
ISSN
0302-9743
eISSN
1611-3349
Page URI
https://pub.uni-bielefeld.de/record/2982112
Zitieren
Hammer B, Schleif F-M, Zhu X. Relational Extensions of Learning Vector Quantization. In: Lu B-L, Zhang L, Kwok J, eds. Neural Information Processing. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2011: 481-489.
Hammer, B., Schleif, F. - M., & Zhu, X. (2011). Relational Extensions of Learning Vector Quantization. In B. - L. Lu, L. Zhang, & J. Kwok (Eds.), Lecture Notes in Computer Science. Neural Information Processing (pp. 481-489). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_56
Hammer, Barbara, Schleif, Frank-Michael, and Zhu, Xibin. 2011. “Relational Extensions of Learning Vector Quantization”. In Neural Information Processing, ed. Bao-Liang Lu, Liqing Zhang, and James Kwok, 481-489. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg.
Hammer, B., Schleif, F. - M., and Zhu, X. (2011). “Relational Extensions of Learning Vector Quantization” in Neural Information Processing, Lu, B. - L., Zhang, L., and Kwok, J. eds. Lecture Notes in Computer Science (Berlin, Heidelberg: Springer Berlin Heidelberg), 481-489.
Hammer, B., Schleif, F.-M., & Zhu, X., 2011. Relational Extensions of Learning Vector Quantization. In B. - L. Lu, L. Zhang, & J. Kwok, eds. Neural Information Processing. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 481-489.
B. Hammer, F.-M. Schleif, and X. Zhu, “Relational Extensions of Learning Vector Quantization”, Neural Information Processing, B.-L. Lu, L. Zhang, and J. Kwok, eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp.481-489.
Hammer, B., Schleif, F.-M., Zhu, X.: Relational Extensions of Learning Vector Quantization. In: Lu, B.-L., Zhang, L., and Kwok, J. (eds.) Neural Information Processing. Lecture Notes in Computer Science. p. 481-489. Springer Berlin Heidelberg, Berlin, Heidelberg (2011).
Hammer, Barbara, Schleif, Frank-Michael, and Zhu, Xibin. “Relational Extensions of Learning Vector Quantization”. Neural Information Processing. Ed. Bao-Liang Lu, Liqing Zhang, and James Kwok. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. Lecture Notes in Computer Science. 481-489.
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