图 书 馆
机器学习中的边界超额风险Bounding Excess Risk in Machine Learning |
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| 课程网址: | http://videolectures.net/mlss09us_koltchinskii_berml/ |
| 主讲教师: | Vladimir Koltchinskii |
| 开课单位: | 佐治亚理工学院 |
| 开课时间: | 2009-07-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We will discuss a general approach to the problem of bounding the excess risk of learning algorithms based on empirical risk minimization (possibly penalized). This approach has been developed in the recent years by several authors (among others: Massart; Bartlett, Bousquet and Mendelson; Koltchinskii). It is based on powerful concentration inequalities due to Talagrand as well as on a variety of tools of empirical processes theory (comparison inequalities, entropy and generic chaining bounds on Gaussian, empirical and Rademacher processes, etc.). It provides a way to obtain sharp excess risk bounds in a number of problems such as regression, density estimation and classification and for many different classes of learning methods (kernel machines, ensemble methods, sparse recovery). It also provides a general way to construct sharp data dependent bounds on excess risk that can be used in model selection and adaptation problems. |
| 关 键 词: | 风险最小化; 超额风险界限; 密度估计; 稀疏恢复; 模型选择 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-05-29：吴雨秋(课程编辑志愿者) |
| 阅读次数: | 326 |