Multi-task proximal support vector machine

Multi-task proximal support vector machine

With the explosive growth of the use of imagery, visual recognition plays an important role in many applications and attracts increasing research attention. Given several related tasks, single-task learning learns each task separately and ignores the relationships among these tasks. Different from single-task learning, multi-task learning can explore more information to learn all tasks jointly by using relationships among these tasks. In this paper, we propose a novel multi-task learning model based on the proximal support vector machine. The proximal support vector machine uses the large-margin idea as does the standard support vector machines but with looser constraints and much lower computational cost. Our multi-task proximal support vector machine inherits the merits of the proximal support vector machine and achieves better performance compared with other popular multi-task learning models. Experiments are conducted on several multi-task learning datasets, including two classification datasets and one regression dataset. All results demonstrate the effectiveness and efficiency of our proposed multi-task proximal support vector machine. (C) 2015 Elsevier Ltd. All rights reserved.

随着图像使用的爆炸式增长，视觉识别在许多应用中起着重要作用，并吸引了越来越多的研究关注。给定几个相关任务，单任务学习分别学习每个任务，忽略了这些任务之间的关系。与单任务学习不同，多任务学习可以通过利用这些任务之间的关系探索更多信息来联合学习所有任务。在本文中，我们提出了一种基于近邻支持向量机的新型多任务学习模型。近邻支持向量机像标准支持向量机一样使用大间隔思想，但具有更宽松的约束和低得多的计算成本。我们的多任务近邻支持向量机继承了近邻支持向量机的优点，与其他流行的多任务学习模型相比取得了更好的性能。在几个多任务学习数据集上进行了实验，包括两个分类数据集和一个回归数据集。所有结果都证明了我们提出的多任务近邻支持向量机的有效性和高效性。(C) 2015爱思唯尔有限公司。保留所有权利。