核方法的简介Introduction to Kernel Methods |
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课程网址: | http://videolectures.net/bootcamp2010_ralaivola_ikm/ |
主讲教师: | Liva Ralaivola |
开课单位: | 艾克斯-马赛大学 |
开课时间: | 2010-08-05 |
课程语种: | 英语 |
中文简介: | 在本文中,我们将看到内核方法的基础知识。在简要介绍了一个非常简单的核分类器之后,我们将给出一个后定核的定义,并解释支持向量机学习。然后,将描述一些结构化数据的内核,即序列和图。本文给出了表示器定理,解释了在处理核方法时遇到的一般核展开的基本原理。最后,给出了统计学习理论中的几个要素。 |
课程简介: | In this talk, we are going to see the basics of kernels methods. After a brief presentation of a very simple kernel classifier, we'll give the definition of a postive definite kernel and explain Support vector machine learning. Then, a few kernels for structured data, namely sequences and graphs, will be described. The representer theorem is presented, which explains the rationale for the usual kernel expansion encountered when working with kernel methods. Finally, a few elements from statistical learning theory are given. |
关 键 词: | 核方法; 核分类器; 向量; 核扩展 |
课程来源: | 视频讲座网 |
最后编审: | 2020-04-08:cjy |
阅读次数: | 59 |