Improved Nonparallel Hyperplanes Support Vector Machines for Multi-class Classification

F. Bai, Ruijie Liu
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引用次数: 2

Abstract

In this paper, we present an improved nonparallel hyperplanes classifier for multi-class classification, termed as INHCMC. As in the nonparallel support vector machine (NPSVM) for binary classification, the ε-insensitive loss function is adopted in the primal problems of multi-class classification to improve the sparseness associated with the nonparallel hyperplanes classifier for multi-class classification (NHCMC) where the quadratic loss function is used. Experimental results on some benchmark datasets are reported to show the effectiveness of our method in terms of sparseness and classification accuracy.
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多类分类的改进非并行超平面支持向量机
本文提出了一种改进的非并行超平面多类分类器INHCMC。与二值分类的非并行支持向量机(NPSVM)一样,在多类分类的原始问题中采用ε-不敏感损失函数,以提高非并行超平面多类分类器(NHCMC)的稀疏性。在一些基准数据集上的实验结果表明,我们的方法在稀疏度和分类精度方面是有效的。
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