From Samples to Objects in Kernel Methods
This paper presents a general method for incorporating prior knowledge into kernel methods. It applies when the prior knowledge can be formalized by the description of an object around each sample of the training set, assuming that all points in the given object share the same desired class. Two implementation techniques of this method, based on analytical kernel jittering and the vicinal risk minimization principle, are considered. Empirical results on one artificial dataset and one real dataset based on EEG signals demonstrate the performance of the proposed method.