Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Category detection using hierarchical mean shift
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Active learning with statistical models
Journal of Artificial Intelligence Research
Selective supervision: guiding supervised learning with decision-theoretic active learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Incremental one-class learning with bounded computational complexity
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Cost-Sensitive Active Visual Category Learning
International Journal of Computer Vision
RADAR: rare category detection via computation of boundary degree
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Finding rare classes: adapting generative and discriminative models in active learning
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Video Behaviour Mining Using a Dynamic Topic Model
International Journal of Computer Vision
Finding Rare Classes: Active Learning with Generative and Discriminative Models
IEEE Transactions on Knowledge and Data Engineering
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For learning problems where human supervision is expensive, active query selection methods are often exploited to maximise the return of each supervision. Two problems where this has been successfully applied are active discovery --- where the aim is to discover at least one instance of each rare class with few supervisions; and active learning --- where the aim is to maximise a classifier's performance with least supervision. Recently, there has been interest in optimising these tasks jointly, i.e., active learning with undiscovered classes, to support efficient interactive modelling of new domains. Mixtures of active discovery and learning and other schemes have been exploited, but perform poorly due to heuristic objectives. In this study, we show with systematic theoretical analysis how the previously disparate tasks of active discovery and learning can be cleanly unified into a single problem, and hence are able for the first time to develop a unified query algorithm to directly optimise this problem. The result is a model which consistently outperforms previous attempts at active learning in the presence of undiscovered classes, with no need to tune parameters for different datasets.