Abstract
The main advantage of the kernel methods is the possibility of using linear models in a nonlinear subspace by an implicit transformation of patterns to a high-dimensional feature space without computing their images directly. An appropriately constructed kernel results in a model that fits well to the structure underlying data and doesn’t over-fit to the sample. Recent state-of-the-art kernel evaluation measures are examined in this paper and their application in kernel optimization is verified. Alternative evaluation measures that outperform presented methods are proposed.Optimization leveraging these measures results in parameters corresponding to the classifiers that achieve minimal error rate for RBF kernel.
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Chudzian, P. (2011). Radial Basis Function Kernel Optimization for Pattern Classification. In: Burduk, R., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Computer Recognition Systems 4. Advances in Intelligent and Soft Computing, vol 95. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20320-6_11
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DOI: https://doi.org/10.1007/978-3-642-20320-6_11
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