The Kernel-Club Journal-Club.

Time:          Fridays 11am, alternating with AI-Stats seminar

Location:   ICS 432

The purpose of this journal club is to read and discuss a number of landmark papers on kernel methods.
The first session will consist of a general introduction to kernel methods. During this first session we will
interactively choose papers from the literature on kernel methods. Each session has one "chair" who
commits to reading the paper in some detail and leads the discussion.
This is not a faculty only event! Students at any level are encouraged to sit in and listen and/or contribute.

If you want to be added or removed from to the mailing list please send an email to

For a very good reference web-source on kernel-machines see:

This quarter Liva Ralaivola and Pierre Baldi teach a (ICS280) class on "Kernels Methods for Bioinformatics".

This winter Max Welling will teach a class (ICS 273B) on "Kernel Methods".


Oct. 01 '04.   Max Welling. (powerpoint slides)
Introduction to Kernels, Chapters 3&4 from Shawe-Taylor and Christianini book: Kernel Methods for Pattern Analysis
suggested background paper to read:
K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf.
An introduction to kernel-based learning algorithms.
IEEE Neural Networks, 12(2):181-201, May 2001
(electronic copy: pdf)

Oct. 08 '04.   Gang Liang (Gang's notes on the kernel learning paper)
This weeks paper is:
G. Lancriet, N. Christianini, B. Bartlett, L. El Ghaoui, M. Jordan
Learning the Kernel with semi-definite programming
ICML2002 (electronic copy: pdf)

Oct. 15 '04.  A. Nonymous.
P. Bartlett, M. Collins, B. Taskar and D. McAllester
Exponentiated gradient algorithms for large-margin structured classification
NIPS16 2003 (electronic copy: ps)

B. Taskar, C. Guestrin and D. Koller
Max-Margin Markov Networks
NIPS17 2004 (electronic copy: ps)

Oct. 29 '04.  Scott White (Scott’s notes on cluster kernel paper)
O. Chappelle, J. Weston and B. Scholkopf
Cluster Kernels for Semi-Supervised Learning
NIPS15 2002
(electronic copy: ps)

Nov. 12 '04. Liva Ralaivola & Pierre Baldi - Kernels for Bioinformatics
C. Leslie, E. Eskin, and W. S. Noble.
The spectrum kernel: Astring kernel for SVM protein classification.
In Proceedings of the Pacific Symposium on Biocomputing, pages 564--575, 2002. (electronic copy: pdf)

H.~Kashima, K.~Tsuda, and A.~Inokuchi.
Marginalized Kernels between Labeled Graphs.
In Proc. of the 20th International Conference on Machine Learning,  2003. (electronic copy: pdf)

J. Ramon and T. Gaertner.
Expressivity versus Efficiency of Graph Kernels.
In Proceedings of the First International Workshop on Mining Graphs, Trees and Sequences (MGTS-2003),
pages 65--74. ECML/PKDD'03 workshop proceedings, September 2003 (electronic copy: pdf)

A list of possible papers for future reading is:

Bartlett and Mendelsson: Rademacher and Gaussian Complexities, Risk and Structural Bounds.
Cucker and Smale: On the Mathematical Foundations of Learning.
Corinna Cortes, Patrick Haffner, Mehryar Mohri: Rational Kernels, Theory and Algorithms.