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多核学习与SMO算法Multiple Kernel Learning and the SMO Algorithm |
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| 课程网址: | http://videolectures.net/nips2010_varma_mkl/ |
| 主讲教师: | Manik Varma |
| 开课单位: | 微软公司 |
| 开课时间: | 2011-03-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Our objective is to train p-norm Multiple Kernel Learning (MKL) and, more generally, linear MKL regularised by the Bregman divergence, using the Sequential Minimal Optimization (SMO) algorithm. The SMO algorithm is simple, easy to implement and adapt, and efficiently scales to large problems. As a result, it has gained widespread acceptance and SVMs are routinely trained using SMO in diverse real world applications. Training using SMO has been a long standing goa in MKL for the very same reasons. Unfortunately, the standard MKL dual is not differentiable, and therefore can not be optimised using SMO style co-ordinate ascent. In this paper, we demonstrate that linear MKL regularised with the p-norm squared, or with certain Bregma divergences, can indeed be trained using SMO. The resulting algorithm retains both simplicity and efficiency and is significantly faster than the state-of-the-art specialised p-norm MKL solvers. We show that we can train on a hundred thousand kernels in approximately seven minutes and on fifty thousand points in less than half an hour on a single core. |
| 关 键 词: | 序列最小优化; 多核学习; 散度正则化 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-19：cxin |
| 阅读次数: | 71 |