A Learner-Independent Knowledge Transfer Approach to Multi-task Learning

This paper proposes a learner-independent multi-task learning (MTL) scheme in which knowledge transfer (KT) is running beyond the learner. In the proposed KT approach, we use minimum enclosing balls (MEBs) as knowledge carriers to extract and transfer knowledge from one task to another. Since the knowledge presented in MEB can be decomposed as raw data, it can be incorporated into any learner as additional training data for a new learning task to improve the learning rate. The effectiveness and robustness of the proposed KT is evaluated, respectively, on multi-task pattern recognition problems derived from synthetic datasets, UCI datasets, and real face image datasets, using classifiers from different disciplines for MTL. The experimental results show that multi-task learners using KT via MEB carriers perform better than learners without-KT, and this has been successfully applied to different classifiers such as k nearest neighbor and support vector machines.