Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning with Progressive Transductive Support Vector Machine
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
A two-stage approach to domain adaptation for statistical classifiers
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Learning from Multiple Sources
The Journal of Machine Learning Research
Transfer learning from multiple source domains via consensus regularization
Proceedings of the 17th ACM conference on Information and knowledge management
Domain adaptation from multiple sources via auxiliary classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Domain adaptation via transfer component analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Learning Under Covariate Shift
The Journal of Machine Learning Research
Cross-domain sentiment classification via spectral feature alignment
Proceedings of the 19th international conference on World wide web
Automatically extracting polarity-bearing topics for cross-domain sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
On minimum distribution discrepancy support vector machine for domain adaptation
Pattern Recognition
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Domain adaptation (DA) using labeled data from related source domains comes in handy when the labeled patterns of a target domain are scarce. Nevertheless, it is worth noting that when the predictive distribution P(y|x) of the domains differs, which establishes Negative Transfer [19], DA approaches generally fail to perform well. Taking this cue, the Predictive Distribution Matching SVM (PDM-SVM) is proposed to learn a robust classifier in the target domain (referred to as the target classifier) by leveraging the labeled data from only the relevant regions of multiple sources. In particular, a k-nearest neighbor graph is iteratively constructed to identify the regions of relevant source labeled data where the predictive distribution maximally aligns with that of the target data. Predictive distribution matching regularization is then introduced to leverage these relevant source labeled data for training the target classifier. In addition, progressive transduction is adopted to infer the label of target unlabeled data for estimating the predictive distribution of the target domain. Finally, extensive experiments are conducted to illustrate the impact of Negative Transfer on several existing state-of-the-art DA methods, and demonstrate the improved performance efficacy of our proposed PDM-SVM on the commonly used multi-domain Sentiment and Reuters datasets.