图 书 馆
学习内核的样本复杂性The Sample Complexity of Learning the Kernel |
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| 课程网址: | http://videolectures.net/lkasok08_david_tscol/ |
| 主讲教师: | Shai Ben-David |
| 开课单位: | 滑铁卢大学 |
| 开课时间: | 2008-12-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The success of kernel based learning algorithms depends upon the suitability of the kernel to the learning task. Ideally, the choice of a kernel should based on prior information of the learner about the task at hand. However, in practice, kernel parameters are being tuned based on available training data. I will discuss the sample complexity overhead associated with such ”learning the kernel” scenarios. I will address the setting in which the training data for the kernel selection is target labeled examples, as well as settings in which this training is based on different types of data, such as unlabeled examples and examples labeled by a different (but related) tasks. Part of this work is joint with Nati Srebro. |
| 关 键 词: | 内核; 训练数据; 任务标记 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-14：lxf |
| 阅读次数: | 69 |