Learning in the presence of concept drift and hidden contexts
Machine Learning
A Bayesian/Information Theoretic Model of Learning to Learn viaMultiple Task Sampling
Machine Learning - Special issue on inductive transfer
Machine Learning - Special issue on inductive transfer
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
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
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
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
Transferring naive bayes classifiers for text classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Traditional text classification algorithms are based on a basic assumption: the training and test data should hold the same distribution. However, this identical distribution assumption is always violated in real applications. Due to the distribution of test data from target domain and the distribution of training data from auxiliary domain are different, we call this classification problem cross-domain classification. Although most of the training data are drawn from auxiliary domain, we still can obtain a few training data drawn from target domain. To solve the cross-domain classification problem in this situation, we propose a two-stage algorithm which is based on semi-supervised classification. We firstly utilizes labeled data in target domain to filter the support vectors of the auxiliary domain, then uses filtered data and labeled data from target domain to construct a classifier for the target domain. The experimental evaluation on real-world text classification problems demonstrates encouraging results and validates our approach.