Support-Vektor-Methoden zur Analyse hochdimensionaler Daten
We describe recent developments and results of statistical learning theory. In the framework of learning from examples, two factors control generalization ability: explai- ning the training data by a learning machine of a suitable complexity. We describe kernel algorithms in feature spaces as elegant and efficient methods of realizing such machines. Examples thereof are Support Vector Machines (SVM)and Kernel PCA (Principal Component Analysis) . More important than any individual example of a kernel algorithm, however, is the insight that any algorithm that can be cast in terms of dot products can be generalized to a nonlinear setting using kernels. Finally, we illustrate the significance of kernel algorithms by briefly describing industrial and academic applications, in- cluding ones where we obtained benchmark record results.