Abstract
Recently, evolving multiple kernel learning methods have attracted researchers’ attention due to the ability to find the composite kernel with the optimal mapping model in a large high-dimensional feature space. However, it is not suitable to compute the composite kernel in a stationary way for all samples. In this paper, we propose a method of evolving non-stationary multiple kernel learning, in which base kernels are encoded as tree kernels and a gating function is used to determine the weights of the tree kernels simultaneously. Obtained classifiers have the composite kernel with the optimal mapping model and select the most appropriate combined weights according to the input samples. Experimental results on several UCI datasets illustrate the validity of proposed method.
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Wu, P., Yin, Q., Guo, P. (2014). Method of Evolving Non-stationary Multiple Kernel Learning. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_3
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DOI: https://doi.org/10.1007/978-3-319-12640-1_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12639-5
Online ISBN: 978-3-319-12640-1
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