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Anytime Representation Learning
Authors: Zhixiang Xu, Matt Kusner, Gao Huang and Kilian Q. Weinberger
Conference: Proceedings of the 30th International Conference on Machine Learning (ICML-13)
Abstract: Evaluation cost during test-time is becoming increasingly important as many real-world applications need fast evaluation (e.g. web search engines, email spam filtering) or use expensive features (e.g. medical diagnosis). We introduce Anytime Feature Representations (AFR), a novel algorithm that explicitly addresses this trade-off in the data representation rather than in the classifier. This enables us to turn conventional classifiers, in particular Support Vector Machines, into test-time cost sensitive anytime classifiers combining the advantages of anytime learning and large-margin classification.
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