Kernel Methods
Slides
Related content
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.
Description
In this short course I will discuss exponential families, density estimation, and conditional estimators such as Gaussian Process classification, regression, and conditional random fields. The key point is that I will be providing a unified view of these estimation methods. In the second part I will discuss how moment matching techniques in Hilbert space can be used to design two-sample tests and independence tests in statistics. I will describe the basic principles and show how they can be used to correct covariate shift, select features, or merge databases.
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Reviews and comments:
Looks like that part 1 and 3 are the same.
Part 1 has not been uploaded.
As mentioned previously .. part 1 seems to be missing so it was difficult to understand what the basis for the lectures was.
The slides in the link `mlss06tw_smola_km_01.pdf` do not match the lectures.
Write your own review or comment: