图 书 馆
成分嘈杂的逻辑学习Compositional Noisy-Logical Learning |
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| 课程网址: | http://videolectures.net/icml09_yuille_cnll/ |
| 主讲教师: | Yuille Alan L |
| 开课单位: | 加利福尼亚大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We describe a new method for learning the conditional probability distribution of a binary-valued variable from labelled training examples. Our proposed Compositional Noisy-Logical Learning (CNLL) approach learns a noisy-logical distribution in a compositional manner. CNLL is an alternative to the well-known AdaBoost algorithm which performs coordinate descent on an alternative error measure. We describe two CNLL algorithms and test their performance compared to AdaBoost on two types of problem: (i) noisy-logical data (such as noisy exclusive-or), and (ii) four standard datasets from the UCI repository. Our results show that we outperform AdaBoost while using significantly fewer weak classifiers, thereby giving a more transparent classifier suitable for knowledge extraction. |
| 关 键 词: | 条件概率分布; 逻辑学习; 误差测量 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-08：lxf |
| 阅读次数: | 37 |