Reducing samples for accelerating multikernel semiparametric support vector regression

Authors:
Yong-Ping Zhao;Jian-Guo Sun;Xian-Quan Zou
Affiliations:
Department of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China;Department of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China;Department of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

In this paper, the reducing samples strategy instead of classical @n-support vector regression (@n-SVR), viz. single kernel @n-SVR, is utilized to select training samples for admissible functions so as to curtail the computational complexity. The proposed multikernel learning algorithm, namely reducing samples based multikernel semiparametric support vector regression (RS-MSSVR), has an advantage over the single kernel support vector regression (classical @e-SVR) in regression accuracy. Meantime, in comparison with multikernel semiparametric support vector regression (MSSVR), the algorithm is also favorable for computational complexity with the comparable generalization performance. Finally, the efficacy and feasibility of RS-MSSVR are corroborated by experiments on the synthetic and real-world benchmark data sets.