Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Convex Optimization
Tutorial on Practical Prediction Theory for Classification
The Journal of Machine Learning Research
ICML '06 Proceedings of the 23rd international conference on Machine learning
A bound on the label complexity of agnostic active learning
Proceedings of the 24th international conference on Machine learning
Hierarchical sampling for active learning
Proceedings of the 25th international conference on Machine learning
Active learning in the non-realizable case
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Analysis of perceptron-based active learning
COLT'05 Proceedings of the 18th annual conference on Learning Theory
A self-training approach to cost sensitive uncertainty sampling
Machine Learning
Examining multiple potential models in end-user interactive concept learning
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Average-case active learning with costs
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Rademacher Complexities and Bounding the Excess Risk in Active Learning
The Journal of Machine Learning Research
Theoretical Computer Science
Online active inference and learning
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Unbiased online active learning in data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Active learning using on-line algorithms
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Smoothness, Disagreement Coefficient, and the Label Complexity of Agnostic Active Learning
The Journal of Machine Learning Research
Efficient Learning with Partially Observed Attributes
The Journal of Machine Learning Research
Batch Mode Active Learning for Networked Data
ACM Transactions on Intelligent Systems and Technology (TIST)
Multi-domain active learning for text classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Active sampling for entity matching
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Activized learning: transforming passive to active with improved label complexity
The Journal of Machine Learning Research
Importance weighted passive learning
Proceedings of the 21st ACM international conference on Information and knowledge management
Active evaluation of ranking functions based on graded relevance
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
A theory of transfer learning with applications to active learning
Machine Learning
Tuning large scale deduplication with reduced effort
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Querying discriminative and representative samples for batch mode active learning
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Active learning and inference method for within network classification
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Active Sampling for Entity Matching with Guarantees
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Active evaluation of ranking functions based on graded relevance
Machine Learning
An active learning approach to home heating in the smart grid
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Efficient active learning of halfspaces: an aggressive approach
The Journal of Machine Learning Research
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We present a practical and statistically consistent scheme for actively learning binary classifiers under general loss functions. Our algorithm uses importance weighting to correct sampling bias, and by controlling the variance, we are able to give rigorous label complexity bounds for the learning process.