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. The first five papers in the special issue describe extensions of the basic algorithms: Kernel Partial Least Squares Regression in RKHS by Roman Rosipal and Leonard J. Trejo describes the development of kernel partial least squares regression. This technique is similar to kernel PCA or latent semantic kernels, but the projection is chosen by modeling the relationship between input and output variables. The paper compares performance of a number of different projection methods and obtains encouraging results, particularly in terms of the number of dimensions required to obtain a certain level of performance. In Support Vector Clustering, Asa Ben-Hur, David Horn, Hava T. Siegelmann and Vladimir Vapnik present a novel clustering method using Support Vector Machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where the minimal enclosing sphere can be calculated. When mapped back to data space, this sphere can separate into several components, each enclosing a separate cluster of points. A simple algorithm for identifying these clusters is discussed and evaluated experimentally. One-Class SVMs for Document Classification by Larry M. Manevitz and Malik Yousef provides extensive experimentation comparing the SVM approach to one-class classification of text documents with more traditional methods such as nearest neighbour, naive Bayes and one more advanced neural network method based on ‘bottleneck’ compression. The