资源分类
》 资源 》论文》Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss

Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss

2020-02-28 | |  39 |   36 |   0

Abstract

We carefully study how well minimizing convex surrogate loss functions corresponds to minimizing the misclassification error rate for the problem of binary classification with linear predictors. We consider the agnostic setting, and investigate guarantees on the misclassification error of the loss-minimizer in terms of the margin error rate of the best predictor. We show that, aiming for such a guarantee, the hinge loss is essentially optimal among all convex losses.

上一篇：Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling

下一篇：Summarizing topical content with word frequency and exclusivity

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...