A Tutorial Review of RKHS Methods in Machine Learning
Over the last ten years, estimation and learning methods utilizing positive definite kernels have become rather popular, particularly in machine learning. Since these methods have a stronger mathematical slant than earlier machine learning methods (e.g., neural networks), there is also significant interest in the statistical and mathematical community for these methods. The present review aims to summarize the state of the art on a conceptual level. In doing so, we build on various sources (including the books [Vapnik, 1998, Burges, 1998, Cristianini and Shawe-Taylor, 2000, Herbrich, 2002] and in particular [Schölkopf and Smola, 2002]), but we also add a fair amount of recent material which helps unifying the exposition. The main idea of all the described methods can be summarized in one paragraph. Traditionally, theory and algorithms of machine learning and statistics has been very well developed for the linear case. Real world data analysis problems, on the other hand, often requires nonlinear methods to detect the kind of dependences that allow successful prediction of properties of interest. By using a positive definite kernel, one can sometimes have the best of both worlds. The kernel corresponds to a dot product in a (usually high dimensional) feature space. In this space, our estimation methods are linear, but as long as we can formulate everything in terms of kernel evaluations, we never explicitly have to work in the high dimensional feature space.