图 书 馆
严格约束下的Neyman-Pearson分类Neyman-Pearson classification under a strict constraint |
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| 课程网址: | http://videolectures.net/colt2011_rigollet_strict/ |
| 主讲教师: | Philippe Rigollet |
| 开课单位: | 麻省理工学院 |
| 开课时间: | 2011-08-02 |
| 课程语种: | 英语 |
| 中文简介: |  出于异常检测的问题，本文实现了Neyman Pearson范式，以处理具有凸损失的二进制分类中的不对称错误。给定一个有限的分类器集合，我们将它们组合起来并获得一个新的分类器，该分类器同时满足以下两个属性的可能性很高：（i）其类型I错误的概率低于预定水平，并且（ii）具有概率类型II的错误接近可能的最小值。通过最小化受经验约束的经验目标来获得所提出的分类器。该方法的新颖之处在于，显示了该问题输出的分类器满足了对I型错误的原始约束。对约束的严格执行会对II型错误的控制产生有趣的影响，因此我们开发了新技术来处理这种情况。最终，机会约束优化的联系显而易见并得到了研究。 |
| 课程简介: | Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two following properties with high probability: (i), its probability of type I error is below a pre-specified level and (ii), it has probability of type II error close to the minimum possible. The proposed classifier is obtained by minimizing an empirical objective subject to an empirical constraint. The novelty of the method is that the classifier output by this problem is shown to satisfy the original constraint on type I error. This strict enforcement of the constraint has interesting consequences on the control of the type II error and we develop new techniques to handle this situation. Finally, connections with chance constrained optimization are evident and are investigated. |
| 关 键 词: | 二进制分类; 约束优化 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-10-23：zyk |
| 最后编审: | 2020-10-27：yxd |
| 阅读次数: | 68 |