Machine Learning Summer School 2006 - Taipei

Support Vector Machines

author: Chih-Jen Lin, National Taiwan University

Description

Support vector machines (SVM) and kernel methods are important machine learning techniques. In this short course, we will introduce their basic concepts. We then focus on the training and optimization procedures of SVM. Examples demonstrating the practical use of SVM will also be discussed. Basically we focus on classification. If time is allowed, we will also touch SVM regression.

Categories

Top: Computer Science: Machine Learning: Kernel Methods: Support Vector Machines

You might be experiencing some problems with Your Video player.

*Slides*
0:06	Support Vector Machines
0:27	Outline
1:28	Why SVM and Kernel Methods
3:00	Support Vector Classification
4:57	Support Vector Classification01
10:49	Maximal Margin
13:24	Data May Not Be Linearly Separable
16:08	Data May Not Be Linearly Separable01
21:14	Finding the Decision Function
25:33	Kernel Tricks
28:50	Kernel Tricks01
31:14	More about Kernels
34:44	More about Kernels01
35:39	Decision function
38:47	Support Vectors: More Important Data
39:43	Support Vectors: More Important Data01
45:26	Outline
45:34	Deriving the Dual
46:28	Lagrangian Dual
53:56	Lagrangian Dual01
55:50	Lagrangian Dual02
57:22	Lagrangian Dual03
60:45	Lagrangian Dual04
62:26	More about Dual Problems
67:27	More about Dual Problems01

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.