Abstract
This paper presents an algorithm for reducing a classifier’s complexity by pruning support vectors in learning the kernel matrix. The proposed algorithm retains the ‘best’ support vectors such that the span of support vectors, as defined by Vapnik and Chapelle, is as small as possible. Experiments on real world data sets show that the number of support vectors can be reduced in some cases by as much as 85% with little degradation in generalization performance.
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Saradhi, V.V., Karnick, H. (2007). Classifier Complexity Reduction by Support Vector Pruning in Kernel Matrix Learning. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_33
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DOI: https://doi.org/10.1007/978-3-540-73007-1_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73006-4
Online ISBN: 978-3-540-73007-1
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