ITM Web Conf.
Volume 12, 2017The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|Number of page(s)||5|
|Section||Session 5: Information Processing Methods and Techniques|
|Published online||05 September 2017|
A Relevance Vector Machine Prediction Method Based on the Biased Wavelet Kernel Function
National Engineering Laboratory for Fiber Optic, Sensing Technology, Wuhan University of Technology, Wuhan, China ; School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China ; Key Laboratory of Fiber Optic Sensing Technology and Information Processing of Ministry of Education, Wuhan University of Technology, Wuhan, China
Relevance Vector Machine (RVM) is an important learning method in the field of machine learning for its sparsity, global optimality and the ability to solve nonlinear problems by using kernel functions. In this paper, a family of biased wavelets was used to construct the kernel functions of RVM. Biased wavelet have adjustable nonzero mean which makes the kernel of RVM more flexible. With the kernel method of the Centered Kernel Target Alignment (CKTA), the biased parameter was selected to improve the prediction performance of RVM model. The algorithm based on the biased wavelet kernel showed an increased prediction accuracy compared to using wavelet kernel and Cauchy kernel. In short, Relevance Vector Machine with the biased wavelet kernel is a flexible prediction algorithm with high prediction accuracy.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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