Abstract
Traditional kernelised classification methods could not perform well sometimes because of the using of a single and fixed kernel, especially on some complicated data sets. In this paper, a novel optimal double-kernel combination (ODKC) method is proposed for complicated classification tasks. Firstly, data sets are mapped by two basic kernels into different feature spaces respectively, and then three kinds of optimal composite kernels are constructed by integrating information of the two feature spaces. Comparative experiments demonstrate the effectiveness of our methods.
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Wang, F., Zhang, H. (2009). Optimal Double-Kernel Combination for Classification. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_9
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DOI: https://doi.org/10.1007/978-3-642-03070-3_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03069-7
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