Abstract
In this article we extend the global relevance learning vector quantization approach by local metric adaptation to obtain a locally optimized model for classification. In this sense we make a step in the direction of quadratic discriminance analysis in statistics where classwise variance matrices are used for class adapted discriminance functions. We demonstrateb the performance of the model for a medical application.
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Hammer, B., Villmann, T., Schleif, F.M., Albani, C., Hermann, W. (2006). Learning Vector Quantization Classification with Local Relevance Determination for Medical Data. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_63
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DOI: https://doi.org/10.1007/11785231_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35748-3
Online ISBN: 978-3-540-35750-6
eBook Packages: Computer ScienceComputer Science (R0)