论文标题
多任务双支持向量机，带有Universum数据
Multi-task twin support vector machine with Universum data
论文作者
论文摘要
近年来，多任务学习（MTL）已成为机器学习的一个有前途的话题，旨在通过利用有益信息来增强许多相关学习任务的性能。在训练阶段，大多数现有的多任务学习模型完全集中在目标任务数据上，而忽略了目标任务中包含的非目标任务数据。为了解决这个问题，与任何类别的分类问题相对应的Universum数据可以用作培训模型中的先验知识。这项研究着眼于使用Universum数据进行多任务学习的挑战，以采用非目标任务数据，从而可以提高性能。它提出了一个具有Universum Data（UMTSVM）的多任务双支持向量机，并提供了两种解决方案的方法。第一种方法考虑了UMTSVM的双重公式，并试图解决二次编程问题。第二种方法为UMTSVM制定了最小二乘版本，并将其称为LS-UMTSVM，以进一步提高概括性能。 LS-UMTSVM中两个原始问题的解决方案仅简化了求解两个线性方程系统，从而实现了一种非常简单，快速的方法。在几个流行的多任务数据集和医疗数据集上进行的数值实验证明了所提出的方法的效率。
Multi-task learning (MTL) has emerged as a promising topic of machine learning in recent years, aiming to enhance the performance of numerous related learning tasks by exploiting beneficial information. During the training phase, most of the existing multi-task learning models concentrate entirely on the target task data and ignore the non-target task data contained in the target tasks. To address this issue, Universum data, that do not correspond to any class of a classification problem, may be used as prior knowledge in the training model. This study looks at the challenge of multi-task learning using Universum data to employ non-target task data, which leads to better performance. It proposes a multi-task twin support vector machine with Universum data (UMTSVM) and provides two approaches to its solution. The first approach takes into account the dual formulation of UMTSVM and tries to solve a quadratic programming problem. The second approach formulates a least-squares version of UMTSVM and refers to it as LS-UMTSVM to further increase the generalization performance. The solution of the two primal problems in LS-UMTSVM is simplified to solving just two systems of linear equations, resulting in an incredibly simple and quick approach. Numerical experiments on several popular multi-task data sets and medical data sets demonstrate the efficiency of the proposed methods.
