Adaptation Regularization: A General Framework for Transfer Learning

@article{Long2014AdaptationRA,
  title={Adaptation Regularization: A General Framework for Transfer Learning},
  author={Mingsheng Long and Jianmin Wang and Guiguang Ding and Sinno Jialin Pan and Philip S. Yu},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2014},
  volume={26},
  pages={1076-1089}
}
Domain transfer learning, which learns a target classifier using labeled data from a different distribution, has shown promising value in knowledge discovery yet still been a challenging problem. Most previous works designed adaptive classifiers by exploring two learning strategies independently: distribution adaptation and label propagation. In this paper, we propose a novel transfer learning framework, referred to as Adaptation Regularization based Transfer Learning (ARTL), to model them in a… CONTINUE READING
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