A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
IEEE Transactions on Knowledge and Data Engineering
An algorithm on multi-view adaboost
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
View construction for multi-view semi-supervised learning
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Multi-view Transfer Learning with Adaboost
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
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Transfer learning, which is one of the most important research directions in machine learning, has been studied in various fields in recent years. In this paper, we combine the theories of multi-source and multi-view learning into transfer learning and propose a new algorithm named Multi-source Transfer Learning with Multi-view Adaboost (MsTL-MvAdaboost). Different from many previous works on transfer learning, in this algorithm, we not only use the labeled data from several source tasks to help learn one target task, but also consider how to transfer them in different views synchronously. We regard all the source and target tasks as a collection of several constituent views and each of these tasks can be learned from different views. Experimental results also validate the effectiveness of our proposed approach.