We propose a new convex loss for Support Vector Machines, both for the binary classification and for the regression models. Therefore, we show the mathematical derivation of the dual problems and we experiment with them on several small datasets. The minimal dimension of those datasets is due to the difficult scalability of the SVM method to bigger instances. This preliminary study should prove that using pattern correlations inside the loss function could enhance the generalisation performances. Our method consistently achieved comparable or superior performance, with improvements of up to 2.0% in F1 scores for classification tasks and 1.0% reduction in Mean Squared Error (MSE) for regression tasks across various datasets, compared to standard losses. Coherently, results show that generalisation measures are never worse than the standard losses and several times they are better. In our opinion, it should be considered a careful study of this loss, coupled with shallow and deep neural networks. In fact, we present some novel results obtained with those architectures.

翻译：本文针对支持向量机的二分类与回归模型，提出了一种新的凸损失函数。为此，我们展示了其对偶问题的数学推导，并在多个小型数据集上进行了实验。选择这些小型数据集是由于支持向量机方法难以扩展至更大规模实例。此项初步研究旨在证明，在损失函数中利用模式相关性可提升泛化性能。与标准损失函数相比，我们的方法在多个数据集上始终取得相当或更优的性能：在分类任务中F1分数最高提升2.0%，在回归任务中均方误差（MSE）最高降低1.0%。结果一致表明，其泛化度量从不逊于标准损失函数，且多次表现更优。我们认为，有必要结合浅层与深度神经网络对此损失函数展开深入研究。事实上，我们已利用这些架构获得了一些新的结果。