Multi-task proximal support vector machine

作者：Li, Ya; Tian, Xinmei^*; Song, Mingli; Tao, Dacheng

来源：Pattern Recognition, 2015, 48(10): 3249-3257.

DOI：10.1016/j.patcog.2015.01.014

• Multi-task learning
• Support vector machines
• Proximal classifiers

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.

• 出版日期2015-10
• 单位中国科学技术大学; 浙江大学