Reproducing Polynomial Kernel Extreme Learning Machine
Yibo Li, Chao Liu, Senyue Zhang, Wenan Tan, Yanyan Ding · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2017
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.