Nov, 2006
Global convergence of decomposition learning methods for support vector machines
IEEE TRANSACTIONS ON NEURAL NETWORKS
- ,
- Volume
- 17
- Number
- 6
- First page
- 1362
- Last page
- 1369
- Language
- English
- Publishing type
- Research paper (scientific journal)
- DOI
- 10.1109/TNN.2006.880584
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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.
- Link information
- ID information
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- DOI : 10.1109/TNN.2006.880584
- ISSN : 1045-9227
- Web of Science ID : WOS:000241933100002