CSCE 990 - Support Vector Machines (Spring 2006) lecture slides
In general, also see:
- Bernhard Schölkopf's
PhD thesis
and his
tutorial slides
- An
introduction to kernel-based learning algorithms by Klaus-Robert
Muller, Sebastian Mika, Gunnar Ratsch, Koji Tsuda, and
Bernhard Schölkopf. IEEE Transactions on Neural Networks,
12(2):181-201 (2001).
- A Tutorial on
Support Vector Machines for Pattern Recognition by Christopher Burges.
Data Mining and Knowledge Discovery, 2:121-167 (1998).
-
An Introduction to Support Vector Machines, by
Nello Cristianini
and John Shawe-Taylor.
Cambridge University Press, 2000.
ISBN 0-521-78019-5.
-
Learning Kernel
Classifiers, by Ralf Herbrich. MIT Press, 2002, ISBN 0-262-08306-X.
-
Machine Learning,
by Tom Mitchell. McGraw-Hill, 1997,
ISBN 0070428077.
-
Lectures 0 and 1: Administrivia and Introduction, Jan 10-12, Chapter 1
(pdf1up,
pdf4up)
- Homework 0 due Jan 17
-
Lecture 2: Kernels,
Jan 17, 24, Sections 1.1, 2.1-2.3, 2.5
(pdf1up,
pdf4up)
- Russ Greiner's talk, Jan 19
-
Lecture 3: Risk and Loss Functions,
Jan 24-26, Sections 1.3, 3.1-3.2, 3.5
(pdf1up,
pdf4up)
Topic summary for Lectures 2 and 3 due Feb 2
-
Lecture 4: Statistical Learning Theory,
Jan 26-Feb 2, Sections 1.3, 5.1-5.4, 5.5.3-5.5.6, 5.6-5.7
(pdf1up,
pdf4up)
- Homework 1 due Feb 12
-
Lectures 5 and 6: Regularization and Optimization,
Feb 2-14, Sections 1.4, 6.1-6.3, 6.6, and a little bit of Chapter 4
(pdf1up,
pdf4up)
-
Lecture 7: SVMs for Classification,
Feb 14-Mar 2, Sections 7.1-7.6, 7.8-7.9
(pdf1up,
pdf4up)
-
Lecture 8: Implementation Issues,
Feb 28-Mar 7, Sections 6.2.5, 6.4, 10.1-10.5, 10.7
(pdf1up,
pdf4up)
Also see:
- Homework 2 due Mar 30
- March 13-17: Spring Break
- March 21: Let it snow, let it snow, let it snow!
-
Lecture 9: Designing Kernels,
Mar 23-28, Sections 13.1-13.3, 13.5, assorted papers
(pdf1up,
pdf4up)
Also see:
-
C. Leslie, E. Eskin, and W. Noble.
The spectrum kernel: A string kernel for SVM
protein classification. In Proceedings of the
Pacific Symposium on Biocomputing, pp. 564-575, 2002.
- A. Zien, G. Ratsch, S. Mika, B. Schölkopf, T. Lengauer, and K.-R. Muller.
Engineering kernels that recognize translation initiation sites.
Bioinformatics, 16(9):799-807, 2000.
- "Box kernel" papers by Tao et al.
- Thomas Gärtner, Peter Flach, and Stefan Wrobel. On graph kernels:
Hardness results and efficient algorithms. In Proceedings of the 16th
Annual Conference on Learning Theory and 7th Kernel Workshop, pages
129-143, 2003.
- Thomas Gärtner's ICML 04 tutorial on kernels for structured data
- Homework 3 due Apr 23
-
How to give a good research talk,
April 20, Goldman/Scott slides
(pdf1up, pdf6up)
Also see:
-
Project presentations, Apr 25-27
(dead week)
- Project reports due Apr 30
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Last modified 16 August 2011.