图 书 馆
机器学习中的密度比估计Density Ratio Estimation in Machine Learning |
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| 课程网址: | http://videolectures.net/bbci2012_sugiyama_machine_learning/ |
| 主讲教师: | Masashi Sugiyama |
| 开课单位: | 东京工业大学 |
| 开课时间: | 2012-12-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In statistical machine learning, avoiding density estimation is essential because it is often more difficult than solving a target machine learning problem itself. This is often referred to as Vapnik's principle, and the support vector machine is one of the successful realizations of this principle. Following this spirit, a new machine learning framework based on the ratio of probability density functions has been introduced recently. This density-ratio framework includes various important machine learning tasks such as transfer learning, outlier detection, feature selection, clustering, and conditional density estimation. All these tasks can be effectively and efficiently solved in a unified manner by direct estimating the density ratio without going through density estimation. In this lecture, I give an overview of theory, algorithms, and application of density ratio estimation. |
| 关 键 词: | 密度; 极其学习; 估算 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-11：yxd |
| 最后编审: | 2020-11-11：yxd |
| 阅读次数: | 228 |