@inproceedings{e749b2723fc24d7795a96df775bba707,
title = "Limes-SVM: A Robust Classification Approach Bridging Soft-Margin and Hard-Margin SVMS",
abstract = "We propose an outlier-robust binary-classification method called LiMES-SVM, which is based on the linearly-involved Moreau-enhanced-over-subspace (LiMES) model (Yukawa et al., 2023). LiMES-SVM involves the Moreau-enhanced hinge loss function which is defined by subtracting the Moreau envelope of the hinge loss from the hinge loss it-self, bridging the soft-margin and hard-margin support vector machines (SVMs) parametrically. While the proposed loss is only weakly convex, the whole cost function remains convex under a mild condition. Numerical examples show that the proposed method exhibits remarkable robustness with respect to the rate and magnitude of outliers.",
keywords = "Moreau envelope, convex optimization, hard-margin SVM, robust classification, soft-margin SVM",
author = "Ryotaro Kadowaki and Masahiro Yukawa",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 34th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2024 ; Conference date: 22-09-2024 Through 25-09-2024",
year = "2024",
doi = "10.1109/MLSP58920.2024.10734795",
language = "English",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
booktitle = "34th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2024 - Proceedings",
}