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维度的度量概念及其在学习中的应用A metric notion of dimension and its applications to learning |
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| 课程网址: | http://videolectures.net/icml2010_krauthgamer_amnda/ |
| 主讲教师: | Robert Krauthgamer |
| 开课单位: | 魏茨曼科学研究所 |
| 开课时间: | 2010-07-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Let us define the dimension of a metric space as the minimum k>0 such that every ball in the metric space can be covered by 2^k balls of half the radius. This definition has several attractive features besides being applicable to every metric space. For instance, it coincides with the standard notion of dimension in Euclidean spaces, but captures also nonlinear structures such as manifolds. Metric spaces of low dimension (under the above definition) occur naturally in many contexts. I will discuss recent theoretical results regarding such metric spaces, including questions such as embeddability, dimension reduction, Nearest Neighbor Search, and large-margin classification, the common thread being that low dimension implies algorithmic efficiency. |
| 关 键 词: | 度量空间; 欧几里德空间; 标准维度 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-25：cwx |
| 阅读次数: | 136 |