Global convergence of decomposition learning methods for support vector machines.

Norikazu Takahashi; Tetsuo Nishi

doi:10.1109/tnn.2006.880584

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Global convergence of decomposition learning methods for support vector machines.

Norikazu Takahashi·Tetsuo Nishi

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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.

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Algorithms Artificial Intelligence Information Storage and Retrieval Information Theory Pattern Recognition, Automated Programming, Linear Signal Processing, Computer-Assisted Journal Article Research Support, Non-U.S. Gov't

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Norikazu Takahashi, Tetsuo Nishi. 2006. Global convergence of decomposition learning methods for support vector machines.. https://doi.org/10.1109/tnn.2006.880584

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