Multi-class Support Vector Machine with Maximizing Minimum Margin
This work addresses a known bottleneck in pattern recognition by improving multi-class SVM, offering a potential enhancement for deep learning applications, though it appears incremental relative to prior SVM extensions.
The paper tackles the problem of extending Support Vector Machines (SVM) to multi-class classification by proposing a novel method that maximizes the minimum margin and incorporates pairwise class loss, resulting in demonstrated effectiveness and superiority over existing methods in empirical evaluations.
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 https://github.com/zz-haooo/M3SVM.