图 书 馆
论多标签学习的一致性On the Consistency of Multi-Label Learning |
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| 课程网址: | http://videolectures.net/colt2011_gao_consistency/ |
| 主讲教师: | Wei Gao |
| 开课单位: | 南京大学 |
| 开课时间: | 2011-08-02 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Multi-label learning has attracted much attention during the past few years. Many multilabel learning approaches have been developed, mostly working with surrogate loss functions since multi-label loss functions are usually difficult to optimize directly owing to non-convexity and discontinuity. Though these approaches are effective, to the best of our knowledge, there is no theoretical result on the convergence of risk of the learned functions to the Bayes risk. In this paper, focusing on two well-known multi-label loss functions, i.e., ranking loss and hamming loss, we prove a necessary and sufficient condition for the consistency of multi-label learning based on surrogate loss functions. Our results disclose that, surprisingly, none convex surrogate loss is consistent with the ranking loss. Inspired by the finding, we introduce the partial ranking loss, with which some surrogate functions are consistent. For hamming loss, we show that some recent multi-label learning approaches are inconsistent even for deterministic multi-label classification, and give a surrogate loss function which is consistent for the deterministic case. Finally, we discuss on the consistency of learning approaches which address multi-label learning by decomposing into a set of binary classification problems. |
| 关 键 词: | 多标记学习方法; 替代损失函数; 贝叶斯风险 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-05：lxf |
| 阅读次数: | 123 |