图 书 馆
泛化边界Generalization bounds |
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| 课程网址: | http://videolectures.net/mlss05us_langford_gb/ |
| 主讲教师: | John Langford |
| 开课单位: | 微软研究 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | When a learning algorithm produces a classifier, a natural question to ask is "How well will it do in the future?" To make statements about the future given the past, some assumption must be made. If we make only an assumption that all examples are drawn independently and identically from some (unknown) distribution, we can answer the question. The answer to this question is directly applicable to classifier testing and confidence reporting. It also provides a simple general explanation of "overfitting", and influences algorithm design. |
| 关 键 词: | 学习算法; 分类器测试; 置信报告 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-19：chenjy |
| 最后编审: | 2023-05-11：chenjy |
| 阅读次数: | 61 |