图 书 馆
在高维特征协方差开发在线学习Exploiting feature covariance in high-dimensional online learning |
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| 课程网址: | http://videolectures.net/aistats2010_ma_efcih/ |
| 主讲教师: | Justin Ma |
| 开课单位: | 加利福尼亚大学 |
| 开课时间: | 2010-06-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Some online algorithms for linear classification model the uncertainty in their weights over the course of learning. Modeling the full covariance structure of the weights can provide a significant advantage for classification. However, for high-dimensional, large-scale data, even though there may be many second-order feature interactions, it is computationally infeasible to maintain this covariance structure. To extend second-order methods to high-dimensional data, we develop low-rank approximations of the covariance structure. We evaluate our approach on both synthetic and real-world data sets using the confidence-weighted online learning framework. We show improvements over diagonal covariance matrices for both low and high-dimensional data. |
| 关 键 词: | 线性分类模型; 全协方差结构建模; 对角协方差矩阵 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-17：yumf |
| 阅读次数: | 57 |