¹ School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116 China
² Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190 China

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Abstract—Extreme learning machine is a new learning algorithm for the single hidden layer feedforward neural networks (SLFNs). ELM has been widely used in various fields and applications to overcome the slow training speed and over-fitting problems of the conventional neural network learning algorithms. ELM algorithm is based on the empirical risk minimization, without considering the structural risk and this may lead to over-fitting problems and at the same time, it is with poor controllability and robustness. For these deficiencies, an optimization method is proposed in this paper, a novel extreme learning machine based on hybrid kernel function (HKELM). The method constructs a hybrid kernel function with better performance by fully combining local kernel function strong learning ability and global kernel function strong generalization ability. Compared with traditional ELM, the results show that this method can effectively improve the ELM classification results, avoid local minimum, with better generalization, robustness, controllability and faster learning rate.

Index Terms—Hybrid Kernel Function; Extreme Learning Machine; Global Kernel Function; Local Kernel Function

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Cite: Shifei Ding, Yanan Zhang, Xinzheng Xu, and Lina Bao, " A Novel Extreme Learning Machine Based on Hybrid Kernel Function," Journal of Computers vol. 8, no. 8, pp. 2110-2117, 2013.