图 书 馆
大尺度广义线性模型的凸变分贝叶斯推理Convex Variational Bayesian Inference for Large Scale Generalized Linear Models |
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| 课程网址: | http://videolectures.net/icml09_nickisch_cvb/ |
| 主讲教师: | Hannes Nickisch |
| 开课单位: | 马克斯普朗克研究所 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We show how variational Bayesian inference can be implemented for very large generalized linear models. Our relaxation is proven to be a convex problem for any log-concave model. We provide a generic double loop algorithm for solving this relaxation on models with arbitrary super-Gaussian potentials. By iteratively decoupling the criterion, most of the work can be done by solving large linear systems, rendering our algorithm orders of magnitude faster than previously proposed solvers for the same problem. We evaluate our method on problems of Bayesian active learning for large binary classification models, and show how to address settings with many candidates and sequential inclusion steps. |
| 关 键 词: | 广义线性; 松弛; 数凹模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-24：cwx |
| 阅读次数: | 21 |