图 书 馆
通过类概率估计从损坏的二进制标签中学习Learning from Corrupted Binary Labels via Class-Probability Estimation |
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| 课程网址: | http://videolectures.net/icml2015_menon_corrupted_binary_labels/ |
| 主讲教师: | Aditya Menon |
| 开课单位: | 澳大利亚ICT卓越研究中心 |
| 开课时间: | 2015-09-27 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Many supervised learning problems involve learning from samples whose labels are corrupted in some way. For example, each sample may have some constant probability of being incorrectly labelled (learning with label noise), or one may have a pool of unlabelled samples in lieu of negative samples (learning from positive and unlabelled data). This paper uses class-probability estimation to study these and other corruption processes belonging to the mutually contaminated distributions framework (Scott et al., 2013), with three conclusions. First, one can optimise balanced error and AUC without knowledge of the corruption process parameters. Second, given estimates of the corruption parameters, one can minimise a range of classification risks. Third, one can estimate the corruption parameters using only corrupted data. Experiments confirm the efficacy of class-probability estimation in learning from corrupted labels. |
| 关 键 词: | 监督学习; 分布框架; 类概率估计 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-14：chenjy |
| 最后编审: | 2023-05-11：chenjy |
| 阅读次数: | 20 |