Support Vector Machine (SVM) stands out as a prominent machine learning
technique widely applied in practical pattern recognition tasks. It achieves
binary classification by maximizing the "margin", which represents the minimum
distance between instances and the decision boundary. Although many efforts
have been dedicated to expanding SVM for multi-class case through strategies
such as one versus one and one versus the rest, satisfactory solutions remain
to be developed. In this paper, we propose a novel method for multi-class SVM
that incorporates pairwise class loss considerations and maximizes the minimum
margin. Adhering to this concept, we embrace a new formulation that imparts
heightened flexibility to multi-class SVM. Furthermore, the correlations
between the proposed method and multiple forms of multi-class SVM are analyzed.
The proposed regularizer, akin to the concept of "margin", can serve as a
seamless enhancement over the softmax in deep learning, providing guidance for
network parameter learning. Empirical evaluations demonstrate the effectiveness
and superiority of our proposed method over existing multi-classification
methods.Code is available at this https URL

我们提出了一种新的多类支持向量机方法，通过考虑两两类别损失，并最大化最小的间隔，实现多类别分类。我们的方法在深度学习中类似于 softmax 的概念，可以作为深度学习中网络参数学习的有效增强。实证评估结果表明，我们的方法在现有的多分类方法上具有明显的优势。