发表机构
DAIS（DAIS）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出多种适用于SVM和神经网络的新型凸损失函数，结合粒子群优化算法求解原始SVM问题，经嵌套交叉验证实验，发现新型损失函数对部分数据集的泛化性能无显著影响。
AI 中文摘要
我们提出了多种适用于支持向量机（SVM）和神经网络的新型凸损失函数，将其应用于二分类任务。尽管在对偶SVM模型中利用这些损失函数存在实际局限，但我们可以将其与SVM原始形式及神经网络结合使用。详细来说，采用粒子群优化（Particle Swarm Optimization）算法求解了带有改进损失函数的原始SVM问题。我们证明了所提出的损失函数是标准损失函数的推广，并在多个小型数据集上对其进行了实验。这项初步研究表明，在损失函数中引入模式相关性，理论上可提升部分数据集的泛化性能。为评估每种损失函数的性能，我们采用了嵌套交叉验证（Nested Cross-Validation）流程。实验结果显示，使用或不使用新型损失函数，泛化度量结果一致。
英文摘要
We propose multiple new convex losses for SVM and Neural Networks, applied to binary classification tasks. While there are practical limitations in exploiting them with the dual SVM models, we are able to use them with SVM primal formulation and Neural Networks. In detail, the primal SVM problem with the modified losses has been solved with the Particle Swarm Optimization algorithm. We prove that the proposed losses are a generalization of the standard loss, and we experiment them with several small data-sets. This preliminary study shows that using pattern correlations inside the loss function could in theory enhance the generalization performances on some data-sets. To evaluate the performance of each loss, we adopt a Nested Cross-Validation procedure. Results show that generalization measures are the same with or without the new losses.
CommentsFurther experiments
