Single Directional SMO Algorithm for Least Squares Support Vector Machines | Zendy

Xigao Shao | Zendy; Kun Wu | Zendy; Bifeng Liao | Zendy

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Single Directional SMO Algorithm for Least Squares Support Vector Machines

Author(s) -

Xigao Shao,

Kun Wu,

Bifeng Liao

Publication year - 2013

Publication title -

computational intelligence and neuroscience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.605

H-Index - 52

eISSN - 1687-5273

pISSN - 1687-5265

DOI - 10.1155/2013/968438

Subject(s) - support vector machine , sequential minimal optimization , convergence (economics) , working set , computer science , algorithm , set (abstract data type) , least squares support vector machine , selection (genetic algorithm) , least squares function approximation , decomposition , simple (philosophy) , mathematical optimization , artificial intelligence , mathematics , statistics , ecology , programming language , economic growth , operating system , philosophy , epistemology , estimator , economics , biology

Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs). In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO-) type decomposition methods is proposed. By the new method, we can select a single direction to achieve the convergence of the optimality condition. A simple asymptotic convergence proof for the new algorithm is given. Experimental comparisons demonstrate that the classification accuracy of the new method is not largely different from the existing methods, but the training speed is faster than existing ones.

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