English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Support Vector Machine (SVM) stands out as a prominent machine learningtechnique widely applied in practical pattern recognition tasks. It achievesbinary classification by maximizing the "margin", which represents the minimumdistance between instances and the decision boundary. Although many effortshave been dedicated to expanding SVM for multi-class case through strategiessuch as one versus one and one versus the rest, satisfactory solutions remainto be developed. In this paper, we propose a novel method for multi-class SVMthat incorporates pairwise class loss considerations and maximizes the minimummargin. Adhering to this concept, we embrace a new formulation that impartsheightened flexibility to multi-class SVM. Furthermore, the correlationsbetween the proposed method and multiple forms of multi-class SVM are analyzed.The proposed regularizer, akin to the concept of "margin", can serve as aseamless enhancement over the softmax in deep learning, providing guidance fornetwork parameter learning. Empirical evaluations demonstrate the effectivenessand superiority of our proposed method over existing multi-classificationmethods.Code is available at https://github.com/zz-haooo/M3SVM.

Multi-class Support Vector Machine with Maximizing Minimum Margin