Kernel Methods

author: Alexander J. Smola, Machine Learning Department, Carnegie Mellon University
published: Feb. 25, 2007,   recorded: July 2006,   views: 3561
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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.

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Download slides icon Download slides: mlss06tw_smola_km_01.pdf (464.3┬áKB)


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Reviews and comments:

Comment1 Chandan Datta, August 31, 2007 at 3:04 a.m.:

Looks like that part 1 and 3 are the same.
Part 1 has not been uploaded.


Comment2 M.Prior, September 2, 2007 at 12:47 p.m.:

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.

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