图 书 馆
可混合性是相对于对数损失的贝叶斯风险曲率Mixability is Bayes Risk Curvature Relative to Log Loss |
|
| 课程网址: | http://videolectures.net/colt2011_williamson_risk/ |
| 主讲教师: | Robert C. Williamson |
| 开课单位: | 澳大利亚国立大学 |
| 开课时间: | 2011-08-02 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Mixability of a loss governs the best possible performance when aggregating expert predictions with respect to that loss. The determination of the mixability constant for binary losses is straightforward but opaque. In the binary case we make this transparent and simpler by characterising mixability in terms of the second derivative of the Bayes risk of proper losses. We then extend this result to multiclass proper losses where there are few existing results. We show that mixability is governed by the Hessian of the Bayes risk, relative to the Hessian of the Bayes risk for log loss. We conclude by comparing our result to other work that bounds prediction performance in terms of the geometry of the Bayes risk. Although all calculations are for proper losses, we also show how to carry the results across to improper losses. |
| 关 键 词: | 混合性; 预测损失; 二元损耗; 混合常数; 贝叶斯风险曲率 |
| 课程来源: | 视频讲座网公开课 |
| 最后编审: | 2019-05-26：cwx |
| 阅读次数: | 30 |