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在线学习与再生核Hilbert空间的竞争On-line learning competitive with reproducing kernel Hilbert spaces |
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| 课程网址: | http://videolectures.net/mcslw04_vovk_llcrk/ |
| 主讲教师: | Vladimir Vovk |
| 开课单位: | 伦敦大学学院 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this talk I will describe a new technique for designing competitive on-line prediction algorithms and proving loss bounds for them. The goal of such algorithms is to perform almost as well as the best decision rules in a wide benchmark class, with no assumptions made about the way the observations are generated. However, standard algorithms in this area can only deal with finite-dimensional (often countable) benchmark classes. The new technique gives similar results for decision rules ranging over infinite-dimensional function spaces. It is based on a recent game-theoretic approach to the foundations of probability and, more specifically, on recent results about defensive forecasting. Given the probabilities produced by a defensive forecasting algorithm, which are known to be well calibrated and to have good resolution in the long run, the expected loss minimization principle is used to find a suitable prediction. |
| 关 键 词: | 在线预测算法; 无限维函数空间; 博弈论方法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-10-22：chenxin |
| 阅读次数: | 83 |