Using Sequential Unconstrained Minimization Techniques to simplify SVM solvers
Using Sequential Unconstrained Minimization Techniques to simplify SVM solvers
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DOI：
10.1016/j.neucom.2011.07.010
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发表时间：
2012-02
期刊：
影响因子：
6
通讯作者：
Sachindra Joshi;Jayadeva;Ganesh Ramakrishnan;S. Chandra
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文献类型：
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作者：
Sachindra Joshi;Jayadeva;Ganesh Ramakrishnan;S. Chandra
本文将序贯无约束最小化技术应用于经典L1范数支持向量机和最小二乘支持向量机的经典表达式。我们证明了每个问题都可以作为一个只有框约束的无约束优化问题序列来解决。我们提出了对应于相应SUMT序列中的单个问题的宽松SVM和宽松LSSVM公式。我们还提出了一个类似SMO的算法来解决通过更新单个拉格朗日乘子来工作的松弛公式。与经典的SVM和LSSVM公式相比，这些方法在大型基准数据集上产生了相当或更好的结果，而且速度要快得多。
In this paper, we apply Sequential Unconstrained Minimization Techniques (SUMTs) to the classical formulations of both the classical L1 norm SVM and the least squares SVM. We show that each can be solved as a sequence of unconstrained optimization problems with only box constraints. We propose relaxed SVM and relaxed LSSVM formulations that correspond to a single problem in the corresponding SUMT sequence. We also propose a SMO like algorithm to solve the relaxed formulations that works by updating individual Lagrange multipliers. The methods yield comparable or better results on large benchmark datasets than classical SVM and LSSVM formulations, at substantially higher speeds.