CALL FOR PAPERS

Journal of Machine Learning Research
Special Issue on "New Perspectives on Kernel Based Learning Methods"
http://www.cs.rhbnc.ac.uk/colt/JMLRspecissue.html

Guest Editors:
Nello Cristianini, John Shawe-Taylor, Bob Williamson

Important dates:
Submission deadline: March 15th, 2001
Decision : May 15th, 2001
Final Versions : June 15th, 2001

Submission procedure:
see webpage:
http://www.cs.rhbnc.ac.uk/colt/JMLRspecissue.html

Background:
Recent theoretical advances and experimental results have drawn
considerable attention to the use of kernel functions in learning systems.
Support Vector Machines, Gaussian Processes, kernel PCA, kernel
Gram-Schmidt, Bayes Point Machines, Relevance and Leverage Vector
Machines, are just some of the algorithms that make crucial use of kernels
for problems of classification, regression, density estimation, novelty
detection and clustering.
At the same time as these algorithms have been under development, novel
techniques specifically designed for kernel-based systems have resulted in
methods for assessing generalisation, implementing model selection, and
analysing performance.
The choice of model may be simply determined by parameters of the kernel,
as for example the width of a Gaussian kernel. More recently, however,
methods for designing and combining kernels have created a toolkit of
options for choosing a kernel in a particular application.
These methods have extended the applicability of the techniques beyond
the natural Euclidean spaces to more general discrete structures. The
field is witnessing growth on a number of fronts, with the publication of
books, editing of special issues, organization of special sessions and
web-sites.
Moreover, a convergence of ideas and concepts from different disciplines
is occurring.

This special issue will accept papers in any of the following main
research directions:

1) design of novel kernel-based algorithms
2) design of novel types of kernel functions
3) development of new learning theory concepts
4) application of the techniques to new problem areas

More information at:
http://www.cs.rhbnc.ac.uk/colt/JMLRspecissue.html

Or: nello@dcs.rhbnc.ac.uk