Accuracy at the Top

  • Stanford University Packard
  • Published 2012

Abstract

We introduce a new notion of classification accuracy based on the top ⌧ -quantile values of a scoring function, a relevant criterion in a number of problems arising for search engines. We define an algorithm optimizing a convex surrogate of the corresponding loss, and discuss its solution in terms of a set of convex optimization problems. We also present… (More)

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