资源分类
》 资源 》论文》Scalable Semi-Supervised Aggregation of Classifiers

Scalable Semi-Supervised Aggregation of Classifiers

2020-02-05 | |  919 |   136 |   0

Abstract

 We present and empirically evaluate an efficient algorithm that learns to aggregate the predictions of an ensemble of binary classifiers. The algorithm uses the structure of the ensemble predictions on unlabeled data to yield significant performance improvements. It does this without making assumptions on the structure or origin of the ensemble, without parameters, and as scalably as linear learning. We empirically demonstrate these performance gains with random forests.

上一篇：Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation

下一篇：Inverse Reinforcement Learning with Locally Consistent Reward Functions

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...