图 书 馆
高阶正则化的半监督学习Semi-supervised Learning by Higher Order Regularization |
|
| 课程网址: | http://videolectures.net/aistats2011_zhou_learning/ |
| 主讲教师: | Xueyuan Zhou |
| 开课单位: | 芝加哥大学 |
| 开课时间: | 2011-05-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In semi-supervised learning, at the limit of infinite unlabeled points while fixing labeled ones, the solutions of several graph Laplacian regularization based algorithms were shown by Nadler et al. (2009) to degenerate to constant functions with "spikes" at labeled points in Rd for d ¸ 2. These optimization problems all use the graph Laplacian regularizer as a common penalty term. In this paper, we address this problem by using regularization based on an iterated Laplacian, which is equivalent to a higher order Sobolev semi-norm. Alternatively, it can be viewed as a generalization of the thin plate spline to an unknown submanifold in high dimensions. We also discuss relationships between Reproducing Kernel Hilbert Spaces and Green's functions. Experimental results support our analysis by showing consistently improved results using iterated Laplacians. |
| 关 键 词: | 离散无限逻辑; 随机变量; 贝叶斯算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-20：lxf |
| 阅读次数: | 28 |