Kernel methods and Support Vector Machines

author: Alexander J. Smola, Google, Inc.
published: March 11, 2008,   recorded: March 2008,   views: 11893
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Watch Part 1
Part 1: Basics and Probability theory 54:04
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Part 2: Instance Based Estimation 42:56
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Watch Part 3
Part 3: Perceptron and Kernels 51:59
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Part 4: Support Vector classification 46:32
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Part 5: Novelty Detection and Regression 52:02
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Part 6: Structured Estimation 1:04:29
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Description

The tutorial will introduce the main ideas of statistical learning theory, support vector machines, and kernel feature spaces. This includes a derivation of the support vector optimization problem for classification and regression, the v-trick, various kernels and an overview over applications of kernel methods.

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Download slides icon Download slides: mlss08au_smola_ksvm.pdf (14.2┬áMB)


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Reviews and comments:

Comment1 farquad, August 20, 2008 at 8:22 a.m.:

its very interesting to listen lectures, but if I could download the videos it would be easy to understand when offline.
Kindly suggest me how i can download the video lectures..

Thank you...


Comment2 Max Dama, March 2, 2009 at 9:10 p.m.:

Smola is one of the clearest lecturers, writers, and thinkers.


Comment3 Cosmin, August 1, 2009 at 10:59 a.m.:

The third video doesn't work.


Comment4 Hamed, March 2, 2011 at 2:12 p.m.:

I wish I could download it


Comment5 Nitin, July 12, 2011 at 3:08 p.m.:

lecture slides not available for L4: Support Vector classification
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