An Online Stochastic Kernel Machine for Robust Signal Classification

We present a novel variation of online kernel machines in Early work on the theoretical limits of online learning which we exploit a consensus based optimization mechanism were investigated from a statistical physics point of view [8]. to guide the evolution of decision functions drawn from a Though these techniques benchmark the performance of reproducing kernel Hilbert space (RKHS), which efficiently online algorithms in idealized scenarios they are of limited models the observed stationary process. We derive an practical value because they are based on specific parametric efficient classification algorithm based on these principles statistical models. Furthermore statistical physics based such that our algorithm reduces to traditional online kernel approaches typically require a priori knowledge of machines for the special case in which the consensus based parameters such as generalization error which are not optimization mechanism is switched off. We illustrate the knowable in practice. On the other hand the methods of algorithm's inherent resistance to label and input noise for the statistical learning theory provide greater insight into the case of online classification, and derive relevant regression behavior of online algorithms [9], particularly kernel based bounds. The resulting algorithm can find numerous algorithms in which we are particularly interested.