The content discusses the challenges faced by ν-SVM in training overhead for large-scale problems and introduces a safe screening rule with bi-level optimization. The proposed method aims to screen out inactive samples before training, reducing computational costs while maintaining prediction accuracy. Experimental results on various datasets verify the effectiveness of the proposed approach.
Support vector machine (SVM) is a successful classification method in machine learning, with the ν support vector machine (ν-SVM) being an extension that offers great model interpretability. The proposed safe screening rule aims to address challenges in training overhead for large-scale problems by integrating Karush-Kuhn-Tucker conditions and variational inequalities of convex problems.
Efficient dual coordinate descent methods are developed to further improve computational speed, leading to a unified framework for accelerating SVM-type models. The content also discusses the sparsity of SVM-type models and focuses on sample screening methods for ν-SVM type models.
Key metrics or figures used to support the author's arguments were not explicitly mentioned in the content provided.
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