scholarly journals On the working set selection in gradient projection-based decomposition techniques for support vector machines

2005 ◽  
Vol 20 (4-5) ◽  
pp. 583-596 ◽  
Author(s):  
Thomas Serafini ◽  
Luca Zanni
Keyword(s):  
Support Vector Machines ◽  
Gradient Projection ◽  
Support Vector ◽  
Decomposition Techniques ◽  
Vector Machines ◽  
Working Set Selection ◽  
Working Set

scholarly journals Single Directional SMO Algorithm for Least Squares Support Vector Machines

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Xigao Shao ◽  
Kun Wu ◽  
Bifeng Liao
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Decomposition Methods ◽  
New Method ◽  
Support Vector ◽  
Major Step ◽  
Vector Machines ◽  
Working Set ◽  
A New Technique ◽  
Smo Algorithm

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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