Abstract
In this paper we describe new methods to built a kernel matrix from a collection of kernels for classification purposes using Support Vector Machines (SVMs). The methods build the combination by quantifying, relative to the classification labels, the difference of information among the kernels. The proposed techniques have been successfully evaluated on a variety of artificial and real data sets.
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de Diego, I.M., Moguerza, J.M., Muñoz, A. (2004). Combining Kernel Information for Support Vector Classification. In: Roli, F., Kittler, J., Windeatt, T. (eds) Multiple Classifier Systems. MCS 2004. Lecture Notes in Computer Science, vol 3077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25966-4_10
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DOI: https://doi.org/10.1007/978-3-540-25966-4_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22144-9
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