Scaling kernel-based systems to large data sets

In the form of the support vector machine and Gaussian processes, kernel-ba sed systems are currently very popular approaches to supervised learning. U nfortunately, the computational load for training kernel-based systems incr eases drastically with the size of the training data set, such that these s ystems are not ideal candidates for applications with large data sets. Neve rtheless, research in this direction is very active. In this paper, I revie w some of the current approaches toward scaling kernel-based systems to lar ge data sets.