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JACIII Vol.21 No.5 pp. 795-802
doi: 10.20965/jaciii.2017.p0795
(2017)

Paper:

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Reproducing Polynomial Kernel Extreme Learning Machine

Yibo Li*, Chao Liu*, Senyue Zhang**, Wenan Tan**, and Yanyan Ding*

*School of Automation, Shenyang Aerospace University
Shenyang, Liaoning 110136, China

**College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics
Nanjing, Jiangsu 211106, China

Received:
April 27, 2017
Accepted:
June 19, 2017
Published:
September 20, 2017
Creative Commons License
Keywords:
kernel extreme learning machine, combined kernel function, reproducing kernel function, cuckoo search algorithm
Abstract

Conventional kernel support vector machine (KSVM) has the problem of slow training speed, and single kernel extreme learning machine (KELM) also has some performance limitations, for which this paper proposes a new combined KELM model that build by the polynomial kernel and reproducing kernel on Sobolev Hilbert space. This model combines the advantages of global and local kernel function and has fast training speed. At the same time, an efficient optimization algorithm called cuckoo search algorithm is adopted to avoid blindness and inaccuracy in parameter selection. Experiments were performed on bi-spiral benchmark dataset, Banana dataset, as well as a number of classification and regression datasets from the UCI benchmark repository illustrate the feasibility of the proposed model. It achieves the better robustness and generalization performance when compared to other conventional KELM and KSVM, which demonstrates its effectiveness and usefulness.

Cite this article as:
Y. Li, C. Liu, S. Zhang, W. Tan, and Y. Ding, “Reproducing Polynomial Kernel Extreme Learning Machine,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.5, pp. 795-802, 2017.
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