Convergence Proof of a Sequential Minimal Optimization Algorithm for Support Vector Regression

Convergence Proof of a Sequential Minimal Optimization Algorithm for Support Vector Regression
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DOI：
10.1109/ijcnn.2006.246703
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发表时间：
2006-10
期刊：
The 2006 IEEE International Joint Conference on Neural Network Proceedings
影响因子：
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通讯作者：
Jun Guo;Norikazu Takahashi;T. Nishi
Jun Guo;Norikazu Takahashi;T. Nishi
中科院分区：
其他
文献类型：
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作者：
Jun Guo;Norikazu Takahashi;T. Nishi

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最近Flake和Lawrence提出了一种支持向量回归（SVR）的序列最小优化（SMO）算法。然而，他们的算法的收敛性至今尚未得到证明。在本文中，我们考虑一个SMO算法，它处理相同的优化问题的Flake和Lawrence的SMO，并给出了一个严格的证明，它总是停止在有限的迭代次数。
A sequential minimal optimization (SMO) algorithm for support vector regression (SVR) has recently been proposed by Flake and Lawrence. However, the convergence of their algorithm has not been proved so far. In this paper, we consider an SMO algorithm, which deals with the same optimization problem as Flake and Lawrence's SMO, and give a rigorous proof that it always stops within a finite number of iterations.