Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Logistic regression with an auxiliary data source
ICML '05 Proceedings of the 22nd international conference on Machine learning
Understanding the Yarowsky Algorithm
Computational Linguistics
Constructing informative priors using transfer learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Self-taught learning: transfer learning from unlabeled data
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
Making generative classifiers robust to selection bias
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
Nonlinear Optimization
TRECVID: benchmarking the effectiveness of information retrieval tasks on digital video
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Domain adaptation from multiple sources via auxiliary classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Heterogeneous source consensus learning via decision propagation and negotiation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A risk minimization framework for domain adaptation
Proceedings of the 18th ACM conference on Information and knowledge management
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Predictive distribution matching SVM for multi-domain learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Multi-source domain adaptation and its application to early detection of fatigue
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A pseudo relevance feedback based cross domain video concept detection
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Pairwise cross-domain factor model for heterogeneous transfer ranking
Proceedings of the fifth ACM international conference on Web search and data mining
On minimum distribution discrepancy support vector machine for domain adaptation
Pattern Recognition
Multisource domain adaptation and its application to early detection of fatigue
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
Triplex transfer learning: exploiting both shared and distinct concepts for text classification
Proceedings of the sixth ACM international conference on Web search and data mining
FIDOS: A generalized Fisher based feature extraction method for domain shift
Pattern Recognition
Probability-one homotopy maps for tracking constrained clustering solutions
Proceedings of the High Performance Computing Symposium
OMS-TL: a framework of online multiple source transfer learning
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
An Ensemble Model for Mobile Device based Arrhythmia Detection
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Concept learning for cross-domain text classification: a general probabilistic framework
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Recent years have witnessed an increased interest in transfer learning. Despite the vast amount of research performed in this field, there are remaining challenges in applying the knowledge learnt from multiple source domains to a target domain. First, data from multiple source domains can be semantically related, but have different distributions. It is not clear how to exploit the distribution differences among multiple source domains to boost the learning performance in a target domain. Second, many real-world applications demand this transfer learning to be performed in a distributed manner. To meet these challenges, we propose a consensus regularization framework for transfer learning from multiple source domains to a target domain. In this framework, a local classifier is trained by considering both local data available in a source domain and the prediction consensus with the classifiers from other source domains. In addition, the training algorithm can be implemented in a distributed manner, in which all the source-domains are treated as slave nodes and the target domain is used as the master node. To combine the training results from multiple source domains, it only needs share some statistical data rather than the full contents of their labeled data. This can modestly relieve the privacy concerns and avoid the need to upload all data to a central location. Finally, our experimental results show the effectiveness of our consensus regularization learning.