资源分类
》 资源 》论文》Activized Learning with Uniform Classification Noise

Activized Learning with Uniform Classification Noise

2020-03-02 | |  98 |   56 |   0

Abstract

We prove that for any VC class, it is possible to transform any passive learning algorithm into an active learning algorithm with strong asymptotic improvements in label complexity for every nontrivial distribution satisfying a uniform classification noise condition. This generalizes a similar result proven by (Hanneke, 2009; 2012) for the realizable case, and is the first result establishing that such general improvement guarantees are possible in the presence of restricted types of classification noise.

上一篇：Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment

下一篇：Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events

用户评价
全部评价

热门资源

  • A Mathematical Mo...

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

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Hierarchical Task...

    We extend hierarchical task network planning wi...

  • Shape-based Autom...

    We present an algorithm for automatic detection...