Journal
PATTERN RECOGNITION LETTERS
Volume 33, Issue 5, Pages 568-579Publisher
ELSEVIER
DOI: 10.1016/j.patrec.2011.11.023
Keywords
Opinion mining; Sentimental classification; Boosting; Transfer learning; Transfer learning with multiple sources; Multiple source domains
Categories
Funding
- National Natural Science Foundation of China [60875072]
- International Cooperation Project [2007DFA11530]
- Beijing Natural Science Foundation [4112035]
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Transfer learning aims at adapting a classifier trained on one domain with adequate labeled samples to a new domain where samples are from a different distribution and have no class labels. In this paper, we explore the transfer learning problems with multiple data sources and present a novel boosting algorithm, SharedBoost. This novel algorithm is capable of applying for very high dimensional data such as in text mining where the feature dimension is beyond several ten thousands. The experimental results illustrate that the SharedBoost algorithm significantly outperforms the traditional methods which transfer knowledge with supervised learning techniques. Besides, SharedBoost also provides much better classification accuracy and more stable performance than some other typical transfer learning methods such as the structural correspondence learning (SCL) and the structural learning in the multiple sources transfer learning problems. (C) 2011 Elsevier B.V. All rights reserved.
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