A Novel Sequential Minimal Optimization Algorithm for Support Vector Regression

机译：支持向量回归的新型序贯最小优化算法

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A novel sequential minimal optimization (SMO) algorithm for support vector regression is proposed. This algorithm is based on Flake and Lawrence's SMO in which convex optimization problems with l variables are solved instead of standard quadratic programming problems with 2l variables where l is the number of training samples, but the strategy for working set selection is quite different. Experimental results show that the proposed algorithm is much faster than Flake and Lawrence's SMO and comparable to the fastest conventional SMO.

机译：提出了一种用于支持向量回归的新型序贯最小优化（SMO）算法。该算法基于Flake和Lawrence的SMO，其中解决了带有l个变量的凸优化问题，而不是解决了带有2l个变量的标准二次规划问题，其中l是训练样本的数量，但是工作集选择的策略却大不相同。实验结果表明，该算法比Flake和Lawrence的SMO快得多，并且可与最快的常规SMO相提并论。

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来源
《Neural Information Processing pt.1; Lecture Notes in Computer Science; 4232》|2006年|827-836|共10页
会议地点 Hong Kong(CN)
作者
Jun Guo; Norikazu Takahashi; Tetsuo Nishi;
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作者单位

Department of Computer Science and Communication Engineering, Kyushu Univ., Fukuoka, 812-8581, Japan;

Faculty of Science and Engineering, Waseda Univ., Tokyo, 162-0072, Japan;

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原文格式 PDF
正文语种 eng
中图分类信息处理（信息加工）;
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