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2012 Improving the Solution of Least Squares Support Vector Machines with Application to a Blast Furnace System
Ling Jian, Shuqian Shen, Yunquan Song
J. Appl. Math. 2012(SI10): 1-12 (2012). DOI: 10.1155/2012/949654


The solution of least squares support vector machines (LS-SVMs) is characterized by a specific linear system, that is, a saddle point system. Approaches for its numerical solutions such as conjugate methods Sykens and Vandewalle (1999) and null space methods Chu et al. (2005) have been proposed. To speed up the solution of LS-SVM, this paper employs the minimal residual (MINRES) method to solve the above saddle point system directly. Theoretical analysis indicates that the MINRES method is more efficient than the conjugate gradient method and the null space method for solving the saddle point system. Experiments on benchmark data sets show that compared with mainstream algorithms for LS-SVM, the proposed approach significantly reduces the training time and keeps comparable accuracy. To heel, the LS-SVM based on MINRES method is used to track a practical problem originated from blast furnace iron-making process: changing trend prediction of silicon content in hot metal. The MINRES method-based LS-SVM can effectively perform feature reduction and model selection simultaneously, so it is a practical tool for the silicon trend prediction task.


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Ling Jian. Shuqian Shen. Yunquan Song. "Improving the Solution of Least Squares Support Vector Machines with Application to a Blast Furnace System." J. Appl. Math. 2012 (SI10) 1 - 12, 2012.


Published: 2012
First available in Project Euclid: 3 January 2013

zbMATH: 1264.68129
Digital Object Identifier: 10.1155/2012/949654

Rights: Copyright © 2012 Hindawi


Vol.2012 • No. SI10 • 2012
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