Anytime Representation Learning
Zhixiang Xu, Matt J. Kusner, Gao Ming Huang, Kilian Q. Weinberger · PolyPublie (École Polytechnique de Montréal) · 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. 1.