Analyzing Minimal Complexity Machines
Analyzing Minimal Complexity Machines
复制标题
分析最小复杂度机器
DOI：
10.1109/ijcnn.2019.8852084
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Abe Shigeo
中科院分区：
文献类型：
--
作者：
Shigeo Abe;Shigeo Abe;Shigeo Abe;Abe Shigeo
最小复杂性机（MCM）最小化训练数据和分离超平面之间的最大距离，并示出比传统的支持向量机更好的推广。本文对多模型问题进行了分析，阐明了多模型问题的解非唯一且无界的条件。为了解决线性规划支持向量机（LP SVM）的无界性问题，提出了最小复杂度线性规划支持向量机（MLP SVM）。通过计算机实验，我们证明了MCM的解决方案是无界的，在某些条件下，MLP SVM的推广比LP SVM的两个类和多类问题的大部分。
The minimal complexity machine (MCM) minimizes the maximum distance between training data and the separating hyperplane and is shown to generalize better than the conventional support vector machine. In this paper, we analyze the MCM and clarify the conditions that the solution of MCM is nonunique and unbounded. To resolve the unboundedness, we propose the minimal complexity linear programming support vector machine (MLP SVM), in which the minimization of the maximum distance between training data and the separating hyperplane is added to the linear programming support vector machine (LP SVM). By computer experiments we show that the solution of the MCM is unbounded under some conditions and that the MLP SVM generalizes better than the LP SVM for most of the two-class and multiclass problems.