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MKL结构化的正则化Structured Regularization for MKL |
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| 课程网址: | http://videolectures.net/nipsworkshops2010_obozinski_srf/ |
| 主讲教师: | Guillaume Obozinski |
| 开课单位: | 巴黎高等商学院 |
| 开课时间: | 2011-01-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | It was realized soon after the introduction of Multiple Kernel Learning that ℓ1 - regularization and its groupwise extension are related to MKL by dualization. MKL can therefore be viewed as providing an extension of sparsity to function spaces. However, this extension is not limited ℓ1 - norm. For example, extensions to ℓp norms leads to several forms of non-sparse MKL. In this talk, we will discuss how structured sparsity can generically be introduced in the MKL framework, how existing algorithmic approaches extend to this case and how this leads naturally to the notion of structured functional spaces. |
| 关 键 词: | 正则化; MKL; 结构化稀疏性; 结构功能空间 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-03：毛岱琦(课程编辑志愿者) |
| 阅读次数: | 39 |