Uncertainty sampling and transductive experimental design for active dual supervision

Vikas Sindhwani, Prem Melville, Richard D. Lawrence
2009 Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09  
Dual supervision refers to the general setting of learning from both labeled examples as well as labeled features. Labeled features are naturally available in tasks such as text classification where it is frequently possible to provide domain knowledge in the form of words that associate strongly with a class. In this paper, we consider the novel problem of active dual supervision, or, how to optimally query an example and feature labeling oracle to simultaneously collect two different forms of
more » ... supervision, with the objective of building the best classifier in the most cost effective manner. We apply classical uncertainty and experimental design based active learning schemes to graph/kernel-based dual supervision models. Empirical studies confirm the potential of these schemes to significantly reduce the cost of acquiring labeled data for training high-quality models.
doi:10.1145/1553374.1553496 dblp:conf/icml/SindhwaniML09 fatcat:iqdvno3c2vcxnnkeutfmkv6dzu