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Multi-Label Calibration Memory Support Vector Machine
Yan Ma, Zhen Wang, Lan Bai
45 - 54
10.3233/FAIA240135
Research Article
Frontiers in Artificial Intelligence and Applications
Volume 385: Artificial Intelligence and Human-Computer Interaction
Abstract

Although Rank SVM and its derivative model a novel support vector machine for multi-label classification (SVM-ML) have achieved very good results in multi-label classification problems, neither of these models can achieve zero empirical risk on the training set. Drawing on the memory mechanism used in binary SVM to solve this problem, we propose the MCMSVM model. The experimental results confirm the superiority of MCMSVM in performance on small datasets.

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