资源分类
》 资源 》论文》Better Algorithms for Selective Sampling

Better Algorithms for Selective Sampling

2020-02-27 | |  75 |   44 |   0

Abstract

We study online algorithms for selective sampling that use regularized least squares (RLS) as base classifier. These algorithms typically perform well in practice, and some of them have formal guarantees on their mistake and query rates. We refine and extend these guarantees in various ways, proposing algorithmic variants that exhibit better empirical behavior while enjoying performance guarantees under much more general conditions. We also show a simple way of coupling a generic gradient-based classifier with a specific RLSbased selective sampler, obtaining hybrid algorithms with combined performance guarantees.

上一篇：Adaptively Learning the Crowd Kernel

下一篇：A Three-Way Model for Collective Learning on Multi-Relational Data

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

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

  • Rating-Boosted La...

    The performance of a recommendation system reli...