Introduction to Kernel Methods
published: Aug. 5, 2010, recorded: July 2010, views: 21770
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !