Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Editorial: Special issue on mining low-quality data
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Discriminative learning for differing training and test distributions
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
The matrix stick-breaking process for flexible multi-task 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
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Knowledge transfer via multiple model local structure mapping
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Spectral domain-transfer learning
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Determining Object Safety Using a Multiagent, Collaborative System
SASOW '08 Proceedings of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops
Zero-data learning of new tasks
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Multiple kernel learning improved by MMD
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Multi-view transfer learning with a large margin approach
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
On minimum distribution discrepancy support vector machine for domain adaptation
Pattern Recognition
Detecting ECG abnormalities via transductive transfer learning
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Multi-view discriminant transfer learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Recently there has been increasing interest in the problem of transfer learning, in which the typical assumption that training and testing data are drawn from identical distributions is relaxed. We specifically address the problem of transductive transfer learning in which we have access to labeled training data and unlabeled testing data potentially drawn from different, yet related distributions, and the goal is to leverage the labeled training data to learn a classifier to correctly predict data from the testing distribution. We have derived efficient algorithms for transductive transfer learning based on a novel viewpoint and the Support Vector Machine (SVM) paradigm, of a large margin hyperplane classifier in a feature space. We show that our method can out-perform some recent state-of-the-art approaches for transfer learning on several data sets, with the added benefits of model and data separation and the potential to leverage existing work on support vector machines.