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Luo, J., Qiao, H. and Zhang, B. (2021) Learning with Smooth Hinge Losses. Neurocomputing, 463, 379-387.
https://doi.org/10.1016/j.neucom.2021.08.060

has been cited by the following article:

• TITLE: The SVM Classifier with Quartic Truncated Pinball Loss

  AUTHORS: Lumiao Wang, Ziye Liu

  KEYWORDS: Support Vector Machine, the SVM Classifier with Huberized Truncated Pinball Loss (HTPSVM), the SVM Classifier with Quartic Truncated Pinball Loss (QTPSVM), the Bregman Modified Second APG Method (BAPGs)

  JOURNAL NAME: Applied Mathematics, Vol.16 No.5, May 23, 2025

  ABSTRACT: In the field of machine learning, support vector machine (SVM) is popular for its powerful performance in classification tasks. However, this method could be adversely affected by data containing noise. One of the main reasons is that many loss functions are too sensitive to sample points far from their classes. In this paper, based on the idea of HTPSVM proposed in (Zhu, Song, et al.), we introduce the SVM classifier with quartic truncated pinball loss (QTPSVM), which can be solved by Bregman modified second accelerated proximal gradient method (BAPGs). Numerical experiments show that QTPSVM is more effective on many real data.