图 书 馆
10亿个实例，1000台机器和3.5个小时1 Billion Instances, 1 Thousand Machines and 3.5 Hours |
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| 课程网址: | http://videolectures.net/nipsworkshops09_walker_bitmh/ |
| 主讲教师: | Daniel Walker |
| 开课单位: | 杨百翰大学 |
| 开课时间: | 2010-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Training conditional maximum entropy models on massive data sets requires significant computational resources, but by distributing the computation, training time can be significant reduced. Recent theoretical results have demonstrated conditional maximum entropy models trained by weight mixtures of independently trained models converge at the same rate as traditional distributed schemes, but significantly faster. This efficiency is achieved primarily by reducing network communication costs, a cost not usually considered but actually quite crucial. |
| 关 键 词: | 最大熵模型; 分布式计算; 降低网络通信成本 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-01：wuyq |
| 阅读次数: | 41 |