Abstract
Standard kernel-based classifiers use only a single kernel, but the real-world applications and the recent developments of various kernel methods have emphasized the need to consider a combination of multiple kernels. We propose an evolutionary approach for finding the optimal weights of a combined kernel used by the Support Vector Machines (SVM) algorithm for solving some particular problems. We use a genetic algorithm (GA) for evolving these weights. The numerical experiments show that the evolved combined kernels (ECKs) perform better than the convex combined kernels (CCKs) for several classification problems.
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Dioş, L., Oltean, M., Rogozan, A., Pecuchet, JP. (2007). Improving SVM Performance Using a Linear Combination of Kernels. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_25
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DOI: https://doi.org/10.1007/978-3-540-71629-7_25
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