Introduction to the special issue on kernel methods

This special issue arose from a workshop held at NIPS 2000 on New Directions in Kernel Methods, though not all the submissions received were from talks at the workshop. With the great help of around forty referees we selected the following ten papers from some 28 submissions, an acceptance rate of 36%. The high number of submissions we received illustrates the vitality and popularity of the field of kernel methods in machine learning. We are pleased to be able to support the fledgling Journal of Machine Learning Research in this way and to provide a rapid but refereed route to publication for the papers presented at the workshop less than a year ago. The papers in the special issue cover a wide range of topics in kernel-based learning machines, but mostly reflect three of the main current research directions: exporting the design principles of standard Support Vector Machines to a variety of other algorithms, producing alternative and more efficient implementations, and deepening the theoretical understanding of kernel methods.