图 书 馆
一种用于规则化风险最小化的可伸缩模块凸型求解器A Scalable Modular Convex Solver for Regularized Risk Minimization |
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| 课程网址: | http://videolectures.net/kdd07_teo_asmcs/ |
| 主讲教师: | Choon Hui Teo |
| 开课单位: | 澳大利亚国立大学 |
| 开课时间: | 2007-08-15 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different regularizers. Examples include linear Support Vector Machines (SVMs), Logistic Regression, Conditional Random Fields (CRFs), and Lasso amongst others. This paper describes the theory and implementation of a highly scalable and modular convex solver which solves all these estimation problems. It can be parallelized on a cluster of workstations, allows for data-locality, and can deal with regularizers such as `1 and `2 penalties. At present, our solver implements 20 different estimation problems, can be easily extended, scales to millions of observations, and is up to 10 times faster than specialized solvers for many applications. The open source code is freely available as part of the ELEFANT toolbox. |
| 关 键 词: | 机器学习; 正则化风险函数; 数据本地化 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-09：lxf |
| 阅读次数: | 19 |