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Active learning for large multi-class problems
2009
2009 IEEE Conference on Computer Vision and Pattern Recognition
Scarcity and infeasibility of human supervision for large scale multi-class classification problems necessitates active learning. Unfortunately, existing active learning methods for multi-class problems are inherently binary methods and do not scale up to a large number of classes. In this paper, we introduce a probabilistic variant of the K-Nearest Neighbor method for classification that can be seamlessly used for active learning in multi-class scenarios. Given some labeled training data, our
doi:10.1109/cvprw.2009.5206651
fatcat:uw25r5zxqvgsjjnpnqbug5fm6a