Back to Search
Start Over
On the convergence of a modified version of SVM light algorithm.
- Source :
-
Optimization Methods & Software . Apr-Jun2005, Vol. 20 Issue 2/3, p311-334. 18p. - Publication Year :
- 2005
-
Abstract
- In this work, we consider the convex quadratic programming problem arising in support vector machine (SVM), which is a technique designed to solve a variety of learning and pattern recognition problems. Since the Hessian matrix is dense and real applications lead to large-scale problems, several decomposition methods have been proposed, which split the original problem into a sequence of smaller subproblems. SVM light algorithm is a commonly used decomposition method for SVM, and its convergence has been proved only recently under a suitable block-wise convexity assumption on the objective function. In SVM light algorithm, the size q of the working set, i.e. the dimension of the subproblem, can be any even number. In the present paper, we propose a decomposition method on the basis of a proximal point modification of the subproblem and the basis of a working set selection rule that includes, as a particular case, the one used by the SVM light algorithm. We establish the asymptotic convergence of the method, for any size q ≥ 2 of the working set, and without requiring any further block-wise convexity assumption on the objective function. Furthermore, we show that the algorithm satisfies in a finite number of iterations a stopping criterion based on the violation of the optimality conditions. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10556788
- Volume :
- 20
- Issue :
- 2/3
- Database :
- Academic Search Index
- Journal :
- Optimization Methods & Software
- Publication Type :
- Academic Journal
- Accession number :
- 15715150
- Full Text :
- https://doi.org/10.1080/10556780512331318209