Functional relevance learning in generalized learning vector quantization

Kaestner M, Hammer B, Biehl M, Villmann T (2012)
Neurocomputing 90: 85-95.

Journal Article | Published | English

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Relevance learning in learning vector quantization is a central paradigm for classification task depending feature weighting and selection. We propose a functional approach to relevance learning for high-dimensional functional data. For this purpose we compose the relevance profile by a superposition of only a few parametrized basis functions taking into account the functional character of the data. The number of these parameters is usually significantly smaller than the number of relevance weights in standard relevance learning, which is the number of data dimensions. Thus, instabilities in learning are avoided and an inherent regularization takes place. In addition, we discuss strategies to obtain sparse relevance models for further model optimization. (C) 2012 Elsevier B.V. All rights reserved.
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Kaestner M, Hammer B, Biehl M, Villmann T. Functional relevance learning in generalized learning vector quantization. Neurocomputing. 2012;90:85-95.
Kaestner, M., Hammer, B., Biehl, M., & Villmann, T. (2012). Functional relevance learning in generalized learning vector quantization. Neurocomputing, 90, 85-95.
Kaestner, M., Hammer, B., Biehl, M., and Villmann, T. (2012). Functional relevance learning in generalized learning vector quantization. Neurocomputing 90, 85-95.
Kaestner, M., et al., 2012. Functional relevance learning in generalized learning vector quantization. Neurocomputing, 90, p 85-95.
M. Kaestner, et al., “Functional relevance learning in generalized learning vector quantization”, Neurocomputing, vol. 90, 2012, pp. 85-95.
Kaestner, M., Hammer, B., Biehl, M., Villmann, T.: Functional relevance learning in generalized learning vector quantization. Neurocomputing. 90, 85-95 (2012).
Kaestner, Marika, Hammer, Barbara, Biehl, Michael, and Villmann, Thomas. “Functional relevance learning in generalized learning vector quantization”. Neurocomputing 90 (2012): 85-95.
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