Multi-source transfer learning with multi-view adaboost
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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Transfer learning, serving as one of the most important research directions in machine learning, has been studied in various fields in recent years. In this paper, we integrate the theory of multi-view learning into transfer learning and propose a new algorithm named Multi-View Transfer Learning with Adaboost (MV-TL Adaboost). Different from many previous works on transfer learning, we not only focus on using the labeled data from one task to help to learn another task, but also consider how to transfer them in different views synchronously. We regard both the source and target task as a collection of several constituent views and each of these two tasks can be learned from every views at the same time. Moreover, this kind of multi-view transfer learning is implemented with adaboost algorithm. Furthermore, we analyze the effectiveness and feasibility of MV-TL Adaboost. Experimental results also validate the effectiveness of our proposed approach.