Abstract
The success of kernel methods are dependent on the kernel, thus a choice of a kernel and proper setting of its parameters are crucial importance. Learning a kernel from the data requires evaluation measures to assess the quality of the kernel. In this paper, we propose a new measure named kernel distance target alignment (KDTA). The measure retains the property of state-of-the-art evaluation measures, kernel target alignment (KTA) and feature space-based kernel matrix evaluation measure (FSM), additionally overcomes the limitation of them. Comparative experiments indicate that the new measure is a good indication of the superiority of a kernel and can get better parameter of RBF kernel.
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Wang, P., Dongfeng, C. (2014). Kernel-Distance Target Alignment. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_11
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DOI: https://doi.org/10.1007/978-3-662-45646-0_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45645-3
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