资源分类
》 资源 》论文》Surrogate Losses and Regret Bounds for Cost-Sensitive Classification with Example-Dependent Costs

Surrogate Losses and Regret Bounds for Cost-Sensitive Classification with Example-Dependent Costs

2020-02-27 | |  112 |   57 |   0

Abstract

We study surrogate losses in the context of cost-sensitive classification with exampledependent costs, a problem also known as regression level set estimation. We give sufficient conditions on the surrogate loss for the existence of a surrogate regret bound. Such bounds imply that as the surrogate risk tends to its optimal value, so too does the expected misclassification cost. Our sufficient conditions encompass example-dependent versions of the hinge, exponential, and other common losses. These results provide theoretical justification for some previously proposed surrogate-based algorithms, and suggests others that have not yet been developed.

上一篇：k-DPPs: Fixed-Size Determinantal Point Processes

下一篇：Computational Rationalization: The Inverse Equilibrium Problem

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...