Hybrid Learning Algorithm for Improving Performance of Regression Support Vector Machine 


Vol. 8,  No. 5, pp. 477-484, Oct.  2001
10.3745/KIPSTB.2001.8.5.477


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  Abstract

This paper proposes a hybrid learning algorithm combined momentum and kernel-adatron for improving the performance of regression support vector machine. The momentum is utilized for high-speed convergence by restraining the oscillation in the process of converging to the optimal solution, and the kernel-adatron algorithm is also utilized for the capability of working in nonlinear feature spaces and the simple implementation. The proposed algorithm has been applied to the 1-dimension and 2-dimension nonlinear function regression problems. The simulation results show that the proposed algorithm has better the learning speed and performance of the regression, in comparison with those using quadratic programming and kernel-adatron algorithm.

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[IEEE Style]

Y. H. Cho, C. H. Park, Y. S. park, "Hybrid Learning Algorithm for Improving Performance of Regression Support Vector Machine," The KIPS Transactions:PartB , vol. 8, no. 5, pp. 477-484, 2001. DOI: 10.3745/KIPSTB.2001.8.5.477.

[ACM Style]

Yong Hyun Cho, Chang Hwan Park, and Yong Su park. 2001. Hybrid Learning Algorithm for Improving Performance of Regression Support Vector Machine. The KIPS Transactions:PartB , 8, 5, (2001), 477-484. DOI: 10.3745/KIPSTB.2001.8.5.477.

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