Abstract
Proximity-based classifiers such as RBF-networks andnearest-neighbour classifiers are notoriously sensitive to the metric used to determine distance between samples. In this paper a method for learning such a metric from training data is presented. This algorithm is a generalization of the so called Variable-Kernel Similarity Metric (VSM) Learning, originally proposed by Lowe and is therefore known as Generalized Variable-Kernel Similarity Metric (GVSM) learning. Experimental results show GVSM to be superior to VSM for extremely noisy or cross-correlated data.
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© 2004 Springer-Verlag Berlin Heidelberg
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Naudé, J.J., van Wyk, M.A., van Wyk, B.J. (2004). Generalized Variable-Kernel Similarity Metric Learning. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2004. Lecture Notes in Computer Science, vol 3138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27868-9_86
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DOI: https://doi.org/10.1007/978-3-540-27868-9_86
Publisher Name: Springer, Berlin, Heidelberg
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