Theory and Applications of Kernel Space
author:
Florence d'Alché,
Université Evry Val d'Essonne Genopole
Description
Basics of kernel definitions and theory are first given. Then 3 algorithms are described with ane xplicit reference to the representer theorem: * Support vector Mahcines, * Support Vector Regression and * Kernel Principal Components Analysis. The last course is devoted to examples of kernel design (Mahalanobis kernles and Fisher kernels)
Categories
Top: Computer Science: Machine Learning: Kernel MethodsTop: Computer Science: Machine Learning: Kernel Methods: Support Vector Machines
You might be experiencing some problems with Your Video player.
Lecture rating
| People found this lecture: | ||
| Worth seeing | ||
| because it is: | ||
| Valuable and informative | ||
| Well presented | ||
| Easily understandable | ||
| Acceptably recorded | ||
| You need to login to cast your vote. | ||
Report a problem or upload files
If 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.
Related content
Visitors who watched this lecture also watched...
32 comments
Watch videos: (click on thumbnail to launch)
Part 1
1:33:04
|
Part 2
0:46:24
|
Part 3
0:18:26
|
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
