图 书 馆
机器学习是棘手的障碍？Is Intractability a Barrier for Machine Learning? |
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| 课程网址: | http://videolectures.net/colt2013_arora_barrier/ |
| 主讲教师: | Sanjeev Arora |
| 开课单位: | 普林斯顿大学 |
| 开课时间: | 2013-08-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | One of the frustrations of machine learning theory is that many of the underlying algorithmic problems are provably intractable (e.g., NP-hard or worse) or presumed to be intractable (e.g., the many open problems in Valiant's model). This talk will suggest that this seeming intractability may arise because many models used in machine learning are more general than they need to be. Careful reformulation as well as willingness to consider new models may allow progress. We will use examples from recent work: Nonnegative matrix factorization, Learning Topic Models, ICA with noise, etc. |
| 关 键 词: | 机器理论学习; 非负矩阵分解; 学习主题模型; 带噪声的ICA |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-21：lxf |
| 阅读次数: | 64 |