提出一种新型凸损失函数，提升SVM与神经网络的泛化性能。
Convex Loss Functions for Support Vector Machines (SVMs) and Neural Networks
- 在损失函数中引入模式相关性，优化分类与回归任务。
- 分类F1最高提升2.0%，回归MSE降低1.0%。
- 适用于浅层与深层神经网络，可推广至多类模型。
我们提出一种新的支持向量机凸损失函数，适用于二分类和回归模型。推导了其对偶问题，并在多个小型数据集上进行实验。由于SVM难以扩展到大规模实例，实验规模受限于小数据集。初步研究表明，将模式相关性引入损失函数可提升泛化性能。所提方法在各类数据集上表现稳定，分类任务的F1分数最高提升2.0%，回归任务的均方误差（MSE）降低1.0%。结果表明，该方法的泛化能力始终不低于标准损失，且多次更优。我们认为应进一步研究该损失在浅层与深层神经网络中的应用，文中展示了相关新成果。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。