Anytime Representation Learning


Zhixiang Xu, Matt Kusner, Gao Huang, Kilian Weinberger ;
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):1076-1084, 2013.


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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