Minimal Complexity Support Vector Machines for Pattern Classification
Minimal Complexity Support Vector Machines for Pattern Classification
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DOI：
10.3390/computers9040088
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
S. Abe
中科院分区：
文献类型：
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作者：
S. Abe
最小复杂度机（mcm）最小化VC （Vapnik-Chervonenkis）维数以获得较高的泛化能力。然而，由于正则化项不包含在目标函数中，所以解不是唯一的。为了解决这一问题，本文讨论了MCM与标准支持向量机（L1 SVM）的融合。这是通过最小化L1支持向量机的最大裕度来实现的。我们称之为最小复杂度L1支持向量机（ML1 SVM）。相关的对偶问题具有两倍的对偶变量数量，ML1支持向量机通过交替优化与正则化项和VC维相关的对偶变量来训练。我们使用几个基准数据集将ML1支持向量机与包括L1支持向量机在内的其他类型的支持向量机进行比较，结果表明ML1支持向量机的性能优于或与L1支持向量机相当。
Minimal complexity machines (MCMs) minimize the VC (Vapnik-Chervonenkis) dimension to obtain high generalization abilities. However, because the regularization term is not included in the objective function, the solution is not unique. In this paper, to solve this problem, we discuss fusing the MCM and the standard support vector machine (L1 SVM). This is realized by minimizing the maximum margin in the L1 SVM. We call the machine Minimum complexity L1 SVM (ML1 SVM). The associated dual problem has twice the number of dual variables and the ML1 SVM is trained by alternatingly optimizing the dual variables associated with the regularization term and with the VC dimension. We compare the ML1 SVM with other types of SVMs including the L1 SVM using several benchmark datasets and show that the ML1 SVM performs better than or comparable to the L1 SVM.