A fast SMO training algorithm for support vector regression

Authors:
Haoran Zhang;Xiaodong Wang;Changjiang Zhang;Xiuling Xu
Affiliations:
College of Information Science and Engineering, Zhejiang Normal University, Jinhua, China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

Support vector regression (SVR) is a powerful tool to solve regression problem, this paper proposes a fast Sequential Minimal Optimization (SMO) algorithm for training support vector regression (SVR), firstly gives a analytical solution to the size two quadratic programming (QP) problem, then proposes a new heuristic method to select the working set which leads to algorithm's faster convergence. The simulation results indicate that the proposed SMO algorithm can reduce the training time of SVR, and the performance of proposed SMO algorithm is better than that of original SMO algorithm.