图 书 馆
排序方法在秩函数的概率空间中的统计一致性Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space |
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| 课程网址: | http://videolectures.net/machine_lan_space/ |
| 主讲教师: | Yanyan Lan |
| 开课单位: | 中国科学院 |
| 开课时间: | 2013-01-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This paper is concerned with the statistical consistency of ranking methods. Recently, it was proven that many commonly used pairwise ranking methods are inconsistent with the weighted pairwise disagreement loss (WPDL), which can be viewed as the true loss of ranking, even in a low-noise setting. This result is interesting but also surprising, given that the pairwise ranking methods have been shown very effective in practice. In this paper, we argue that the aforementioned result might not be conclusive, depending on what kind of assumptions are used. We give a new assumption that the labels of objects to rank lie in a rank-differentiable probability space (RDPS), and prove that the pairwise ranking methods become consistent with WPDL under this assumption. What is especially inspiring is that RDPS is actually not stronger than but similar to the low-noise setting. Our studies provide theoretical justifications of some empirical findings on pairwise ranking methods that are unexplained before, which bridge the gap between theory and applications. |
| 关 键 词: | 排名方法; 统计一致性; 秩函数的概率空间; 低噪声环境 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-10-22：chenxin |
| 阅读次数: | 31 |