Abstract
This paper proposes a kind of novel kernel functions obtained from the reproducing kernels of Hilbert spaces associated with special inner product. SVM with the proposed kernel functions only need less support vectors to construct two-class hyperplane than the SVM with Gaussian kernel functions, so the proposed kernel functions have the better generalization. Finally, SVM with reproducing and Gaussian kernels are respectively applied to two benchmark examples: the well-known Wisconsin breast cancer data and artificial dataset.
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Liao, X., Tao, L. (2008). A Class of Novel Kernel Functions. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_24
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DOI: https://doi.org/10.1007/978-3-540-88906-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88905-2
Online ISBN: 978-3-540-88906-9
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