Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Probabilistic latent semantic indexing
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Analyzing the effectiveness and applicability of co-training
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Document clustering with cluster refinement and model selection capabilities
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Composite Kernels for Hypertext Categorisation
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning to learn with the informative vector machine
ICML '04 Proceedings of the twenty-first 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
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Linear prediction models with graph regularization for web-page categorization
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Boosting for transfer learning
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Self-taught learning: transfer learning from unlabeled data
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Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Co-clustering based classification for out-of-domain documents
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An RKHS for multi-view learning and manifold co-regularization
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Spectral domain-transfer learning
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Learning from Multiple Sources
The Journal of Machine Learning Research
Deep transfer via second-order Markov logic
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Co-adaptation: Adaptive co-training for semi-supervised learning
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Mapping and revising Markov logic networks for transfer learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Large margin transductive transfer learning
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Bundle Methods for Regularized Risk Minimization
The Journal of Machine Learning Research
Multiple source adaptation and the Rényi divergence
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Information-theoretic multi-view domain adaptation
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
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
MI2LS: multi-instance learning from multiple informationsources
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Discriminative feature selection for multi-view cross-domain learning
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Multi-view discriminant transfer learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Democracy is good for ranking: towards multi-view rank learning and adaptation in web search
Proceedings of the 7th ACM international conference on Web search and data mining
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Transfer learning has been proposed to address the problem of scarcity of labeled data in the target domain by leveraging the data from the source domain. In many real world applications, data is often represented from different perspectives, which correspond to multiple views. For example, a web page can be described by its contents and its associated links. However, most existing transfer learning methods fail to capture the multi-view {nature}, and might not be best suited for such applications. To better leverage both the labeled data from the source domain and the features from different views, {this paper proposes} a general framework: Multi-View Transfer Learning with a Large Margin Approach (MVTL-LM). On one hand, labeled data from the source domain is effectively utilized to construct a large margin classifier; on the other hand, the data from both domains is employed to impose consistencies among multiple views. As an instantiation of this framework, we propose an efficient optimization method, which is guaranteed to converge to ε precision in O(1/ε) steps. Furthermore, we analyze its error bound, which improves over existing results of related methods. An extensive set of experiments are conducted to demonstrate the advantages of our proposed method over state-of-the-art techniques.