Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework. Nowadays, they often outperform other supervised methods and remain one of the most popular approaches in the machine learning arena. In this work, we investigate the training of SVMs through a smooth sparse-promoting-regularized squared hinge loss minimization. This choice paves the way to the application of quick training methods built on majorization-minimization approaches, benefiting from the Lipschitz differentiabililty of the loss function. Moreover, the proposed approach allows us to handle sparsity-preserving regularizers promoting the selection of the most significant features, so enhancing the performance. Numerical tests and comparisons conducted on three different datasets demonstrate the good performance of the proposed methodology in terms of qualitative metrics (accuracy, precision, recall, and F 1 score) as well as computational cost.

翻译：数十年前，支持向量机（Support Vector Machines, SVMs）被引入用于在监督框架下执行二分类任务。如今，它们通常优于其他监督方法，并至今仍是机器学习领域中最流行的技术之一。本研究探讨了通过光滑稀疏促进正则化的平方铰链损失最小化来训练支持向量机的方法。这一选择为应用基于最大化-最小化方法的快速训练技术铺平了道路，并利用了损失函数的Lipschitz可微性。此外，所提出的方法能够处理保持稀疏性的正则化项，从而促进最具显著性特征的选取，进而提升性能。在三个不同数据集上进行的数值测试与比较表明，所提出的方法在定性指标（准确率、精确率、召回率和F1分数）以及计算成本方面均展现出良好的性能。