A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Representation and learning in information retrieval
Representation and learning in information retrieval
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
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
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
ReCoM: reinforcement clustering of multi-type interrelated data objects
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
IRC: An Iterative Reinforcement Categorization Algorithm for Interrelated Web Objects
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Constructing informative priors using transfer learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Cross-domain knowledge transfer using structured representations
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
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Traditional text classification techniques are based on a basic assumption that the underlying distributions of training and test data should be identical. However, in many real world applications, this assumption is not often satisfied. Labeled training data are expensive, but there may be some labeled data available in a different but related domain from test data. Therefore, how to make use of labeled data from a different domain to supervise the classification becomes a crucial task. In this paper, we propose a novel algorithm for cross-domain text classification using reinforcement learning. In our algorithm, the training process is iteratively reinforced by making use of the relations between documents and words. Empirically, our method is an effective and scalable approach for text categorization when the training and test data are from different but related domains. The experimental results show that our algorithm can achieve better performance than several state-of-art classifiers.