Abstract
Decomposition methods are well-known techniques for solving quadratic programming (QP) problems arising in support vector machines (SVMs). In each iteration of a decomposition method, a small number of variables are selected and a QP problem with only the selected variables is solved. Since large matrix computations are not required, decomposition methods are applicable to large QP problems. In this paper, we will make a rigorous analysis of the global convergence of general decomposition methods for SVMs. We first introduce a relaxed version of the optimality condition for the QP problems and then prove that a decomposition method reaches a solution satisfying this relaxed optimality condition within a finite number of iterations under a very mild condition on how to select variables.
Original language | English |
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Pages (from-to) | 1362-1369 |
Number of pages | 8 |
Journal | IEEE Transactions on Neural Networks |
Volume | 17 |
Issue number | 6 |
DOIs | |
Publication status | Published - Nov 2006 |
Externally published | Yes |
Keywords
- Decomposition method
- Global convergence
- Quadratic programming (QP)
- Support vector machines (SVMs)
- Termination
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence