Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Making large-scale support vector machine learning practical
Advances in kernel methods
A vector space model for automatic indexing
Communications of the ACM
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Co-clustering based classification for out-of-domain documents
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Brief paper: Experience-consistent modeling: Regression and classification problems
Automatica (Journal of IFAC)
Multi-domain sentiment classification
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Cross-domain sentiment classification via spectral feature alignment
Proceedings of the 19th international conference on World wide web
Bridging Domains Using World Wide Knowledge for Transfer Learning
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective
IEEE Transactions on Knowledge and Data Engineering
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Traditional classification methods in machine learning assume that training data and testing data should share the same feature space and have the same data distribution. In real world applications, however, this assumption often does not hold. If there are very few labeled instances in the target domain for training, it is time-consuming to label them manually. In this case, a source domain which has semantic relationships with the target domain but has the different feature space or distribution can be used to assist the classification. In this paper, we propose a new method using rules to help the domain adaptation, which can well represent the knowledge relationships between source domain and target domain. In this algorithm we first discover term-term rules according to the term relationships in target domain to build the knowledge bridge, then we reconstruct the source domain using these rules and get a better classifier to improve the cross-domain classification performance. We conduct several cross-domain data sets and demonstrate that the proposed method is easy to understand and it has a better performance compared to state-of-art transfer algorithms.