关键词: 多任务学习, 大数据集, 核心向量机, 快速分类

Abstract: Regularized multi-task learning (rMTL) method and its extensions have achieved remarkable achievement in theoretical research and applications. However, previous research focuses on the relationship of tasks instead of the complexity of algorithms, the high computational cost of these methods are impractical for large scale datasets. In order to overcome this shortcoming, this paper proposes a fast regularized multi-task learning (FrMTL) based on core vector machine (CVM) theory. FrMTL is competitive with rMTL in classification accuracy while FrMTL-CVM can make a decision rapidly for large scale datasets because of the merit of asymptotic linear time complexity. The effectiveness of the proposed classifier is also experimentally confirmed.

Key words: multi-task learning, large scale dataset, core vector machine, fast classification