Soft Upper-bound Support Vector Machines
Soft Upper-bound Support Vector Machines
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DOI：
10.1109/ijcnn55064.2022.9892425
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发表时间：
2022-07
期刊：
影响因子：
--
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L1支持向量机（ML1 SVM）是将最小复杂度机（MCM）和标准支持向量机（L1 SVM）融合在一起，以实现比L1 SVM更高的泛化能力。通过最小化决策函数值的上界（即，最小化最大余量以及最大化最小余量）。为了避免由于上界极小化而导致泛化能力下降，本文引入了决策函数值的软上界。这是通过允许一些训练数据输出超过上限来实现的。我们称这种架构为软上限L1 SVM（SL1 SVM）。通过引入软上界，超参数的数量增加了一个。我们解释了如何通过适当地设置超参数值来消除增加，并阐明了SL1 SVM的特性。最后，我们比较了SL1 SVM与ML1 SVM使用几个基准数据集，并表明SL1 SVM比ML1 SVM更频繁地发生过拟合。
The minimum complexity L1 SVM (ML1 SVM) is the fusion of the minimal complexity machine (MCM) and the standard support vector machine (L1 SVM) to realize high generalization ability over the L1 SVM. The fusion is realized by minimizing the upper bound on the decision function values (i.e., minimizing the maximum margin as well as maximizing the minimum margin) in the L1 SVM. In this paper, to avoid worsening the generalization ability by minimizing the upper bound, we introduce the soft upper bound on the decision function values. This is realized by allowing some of the training data outputs to exceed the upper bound. We call this architecture soft upper-bound L1 SVM (SL1 SVM). By introducing the soft upper-bound, the number of hyperparameters increases by one. We explain how to cancel the increase by setting the hyperparameter value properly, and clarify the characteristics of the SL1 SVM. Finally, we compare the SL1 SVM with ML1 SVMs using several benchmark data sets and show that overfitting occurs for the SL1 SVM more frequently than for the ML1 SVMs.