Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Discriminant Analysis: A Unified Approach
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Asymptotic Bayesian generalization error when training and test distributions are different
Proceedings of the 24th 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
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
When efficient model averaging out-performs boosting and bagging
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Multiple kernel learning improved by MMD
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
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
Transfer learning with adaptive regularizers
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
On minimum distribution discrepancy support vector machine for domain adaptation
Pattern Recognition
ComSoc: adaptive transfer of user behaviors over composite social network
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Domain transfer dimensionality reduction via discriminant kernel learning
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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
Linear semi-supervised projection clustering by transferred centroid regularization
Journal of Intelligent Information Systems
User behavior learning and transfer in composite social networks
ACM Transactions on Knowledge Discovery from Data (TKDD) - Casin special issue
Machine Vision and Applications
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When labeled examples are limited and difficult to obtain, transfer learning employs knowledge from a source domain to improve learning accuracy in the target domain. However, the assumption made by existing approaches, that the marginal and conditional probabilities are directly related between source and target domains, has limited applicability in either the original space or its linear transformations. To solve this problem, we propose an adaptive kernel approach that maps the marginal distribution of target-domain and source-domain data into a common kernel space, and utilize a sample selection strategy to draw conditional probabilities between the two domains closer. We formally show that under the kernel-mapping space, the difference in distributions between the two domains is bounded; and the prediction error of the proposed approach can also be bounded. Experimental results demonstrate that the proposed method outperforms both traditional inductive classifiers and the state-of-the-art boosting-based transfer algorithms on most domains, including text categorization and web page ratings. In particular, it can achieve around 10% higher accuracy than other approaches for the text categorization problem. The source code and datasets are available from the authors.