The Journal of Machine Learning Research (www.jmlr.org) is pleased to announce the availability of a new paper in electronic form. - ---------------------------------------- Bayes Point Machines Ralf Herbrich, Thore Graepel and Colin Campbell. Journal of Machine Learning Research 1 (August 2001), pp. 245-279. Abstract Kernel-classifiers comprise a powerful class of non-linear decision functions for binary classification. The support vector machine is an example of a learning algorithm for kernel classifiers that singles out the consistent classifier with the largest margin, i.e. minimal real-valued output on the training sample, within the set of consistent hypotheses, the so-called version space. We suggest the Bayes point machine as a well-founded improvement which approximates the Bayes-optimal decision by the centre of mass of version space. We present two algorithms to stochastically approximate the centre of mass of version space: a billiard sampling algorithm and a sampling algorithm based on the well known perceptron algorithm. It is shown how both algorithms can be extended to allow for soft-boundaries in order to admit training errors. Experimentally, we find that - for the zero training error case - Bayes point machines consistently outperform support vector machines on both surrogate data and real-world benchmark data sets. In the soft-boundary/soft-margin case, the improvement over support vector machines is shown to be reduced. Finally, we demonstrate that the real-valued output of single Bayes points on novel test points is a valid confidence measure and leads to a steady decrease in generalisation error when used as a rejection criterion. This paper and earlier papers in Volume 1 are available electronically at http://www.jmlr.org in PostScript, PDF and HTML formats; a bound, hardcopy edition of Volume 1 will be available later this year. - -David Cohn, <[EMAIL PROTECTED]> Managing Editor, Journal of Machine Learning Research ------- This message has been sent to the mailing list "[EMAIL PROTECTED]", which is maintained automatically by majordomo. To subscribe to the list, send mail to [EMAIL PROTECTED] with the line "subscribe jmlr-announce" in the body; to unsubscribe send email to [EMAIL PROTECTED] with the line "unsubscribe jmlr-announce" in the body.
