Machine Learning - Special issue on inductive transfer
Making large-scale support vector machine learning practical
Advances in kernel methods
Proceedings of the 11th international conference on World Wide Web
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Actively Transfer Domain Knowledge
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Domain adaptation with unlabeled data for dialog act tagging
DANLP 2010 Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
Frustratingly easy semi-supervised domain adaptation
DANLP 2010 Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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There is usually an assumption in traditional machine learning that the training and test data are governed by the same distribution. This assumption might be violated when the training and test data come from different time periods or domains. In such situations, traditional machine learning methods not aware of the shift of distribution may fail. This paper proposes a novel algorithm, namely bridged refinement, to take the shift into consideration. The algorithm corrects the labels predicted by a shift-unaware classifier towards a target distribution and takes the mixture distribution of the training and test data as a bridge to better transfer from the training data to the test data. In the experiments, our algorithm successfully refines the classification labels predicted by three state-of-the-art algorithms: the Support Vector Machine, the naïve Bayes classifier and the Transductive Support Vector Machine on eleven data sets. The relative reduction of error rates is about 50% in average.