COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Multi-Task Learning for Classification with Dirichlet Process Priors
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
Domain Adaptation of Conditional Probability Models Via Feature Subsetting
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Bridged Refinement for Transfer Learning
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Representative sampling for text classification using support vector machines
ECIR'03 Proceedings of the 25th European conference on IR research
Multi-class ensemble-based active learning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Relaxed Transfer of Different Classes via Spectral Partition
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Domain adaptation meets active learning
ALNLP '10 Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing
Relevant knowledge helps in choosing right teacher: active query selection for ranking adaptation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Active supervised domain adaptation
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Compact coding for hyperplane classifiers in heterogeneous environment
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Multi-domain active learning for text classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Active learning with transfer learning
ACL '12 Proceedings of ACL 2012 Student Research Workshop
Batch Mode Active Sampling Based on Marginal Probability Distribution Matching
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Active learning for cross-domain sentiment classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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When labeled examples are not readily available, active learning and transfer learning are separate efforts to obtain labeled examples for inductive learning. Active learning asks domain experts to label a small set of examples, but there is a cost incurred for each answer. While transfer learning could borrow labeled examples from a different domain without incurring any labeling cost, there is no guarantee that the transferred examples will actually help improve the learning accuracy. To solve both problems, we propose a framework to actively transfer the knowledge across domains, and the key intuition is to use the knowledge transferred from other domain as often as possible to help learn the current domain, and query experts only when necessary. To do so, labeled examples from the other domain (out-of-domain) are examined on the basis of their likelihood to correctly label the examples of the current domain (in-domain). When this likelihood is low, these out-of-domain examples will not be used to label the in-domain example, but domain experts are consulted to provide class label. We derive a sampling error bound and a querying bound to demonstrate that the proposed method can effectively mitigate risk of domain difference by transferring domain knowledge only when they are useful, and query domain experts only when necessary. Experimental studies have employed synthetic datasets and two types of real world datasets, including remote sensing and text classification problems. The proposed method is compared with previously proposed transfer learning and active learning methods. Across all comparisons, the proposed approach can evidently outperform the transfer learning model in classification accuracy given different out-of-domain datasets. For example, upon the remote sensing dataset, the proposed approach achieves an accuracy around 94.5%, while the comparable transfer learning model drops to less than 89% in most cases. The software and datasets are available from the authors.