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 email@example.com
For a very good reference web-source on
kernel-machines see: www.kernel-machines.org
This winter Max Welling will teach a class (ICS 273B) on "Kernel
Oct. 01 '04.
Max Welling. (powerpoint slides)
Introduction to Kernels, Chapters
3&4 from Shawe-Taylor and Christianini book: Kernel Methods for Pattern
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
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.
P. Bartlett, M. Collins, B. Taskar and
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
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
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
J. Ramon and T. Gaertner.
Expressivity versus Efficiency of Graph Kernels.
In Proceedings of the First International Workshop on Mining Graphs, Trees and
pages 65--74. ECML/PKDD'03 workshop proceedings, September 2003 (electronic copy: pdf)
A list of possible papers for future reading