A novel learning approach to multiple tasks based on boosting methodology

Authors:
Pipei Huang;Gang Wang;Shiyin Qin
Affiliations:
School of Automation Science and Electrical Engineering, Beihang University, 100191 Beijing, China;Machine Learning Group, Microsoft Research Asia, Sigma Center, 49 Zhichun Road, 100190 Beijing, China;School of Automation Science and Electrical Engineering, Beihang University, 100191 Beijing, China
Venue:
Pattern Recognition Letters
Year:
2010

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Abstract

Boosting has become one of the state-of-the-art techniques in many supervised learning and semi-supervised learning applications. In this paper, we develop a novel boosting algorithm, MTBoost, for multi-task learning problem. Many previous multi-task learning algorithms can only solve the problem in low or moderate dimensional space. However, the MTBoost algorithm is capable of working for very high dimensional data such as in text mining where the feature number is beyond several 10,000. The experimental results illustrate that the MTBoost algorithm provides significantly better classification performance than supervised single task learning algorithms. Moreover, MTBoost outperforms some other typical multi-task learning methods.