Do Minimal Complexity Least Squares Support Vector Machines Work?
Do Minimal Complexity Least Squares Support Vector Machines Work?
复制标题
最小复杂度最小二乘支持向量机有效吗？
DOI：
10.1007/978-3-031-20650-4_5
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Shigeo Abe
中科院分区：
文献类型：
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作者：
Yukihiro Harada;Shigeo Asahi;Takashi Kita;Shigeo Abe
最小复杂性支持向量机是支持向量机和最小复杂度机的融合，其结果是最大化最小裕度和最小化最大裕度。改进了L1支持向量机(标准支持向量机)和线性规划支持向量机的泛化能力。在本文中，我们讨论了它是否也适用于最小二乘支持向量机。最小复杂度最小二乘支持向量机(MLS支持向量机)是通过最小化误差平方和和最小化最大误差来训练的。这导致交替地求解一组线性方程和一个二次规划。通过对两类和多类问题的计算机实验，MLS支持向量机在测试数据上的性能并不优于LS支持向量机，但在交叉验证数据上却优于LS支持向量机。
The minimal complexity support vector machine is a fusion of the support vector machine (SVM) and the minimal complexity machine (MCM), and results in maximizing the minimum margin and minimizing the maximum margin. It works to improve the generalization ability of the L1 SVM (standard SVM) and LP (Linear Programming) SVM. In this paper, we discuss whether it also works for the LS (Least Squares) SVM. The minimal complexity LS SVM (MLS SVM) is trained by minimizing the sum of squared margin errors and minimizing the maximum margin. This results in solving a set of linear equations and a quadratic program, alternatingly. According to the computer experiments for two-class and multiclass problems, the MLS SVM does not outperform the LS SVM for the test data although it does for the cross-validation data.