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Special Issue of JMLR

by admin last modified 2007-02-12 10:46


Journal of Machine Learning Research

Special Issue on "New Perspectives on Kernel Based Learning Methods"

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

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

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