Learning performance of kernel SVMC with Markov chain samples

Markov sampling is a natural sampling mechanism extensively used in applications, especially in the study of time sequence or content-based pattern recognition or biological sequence analysis. In this paper we generalize the study on the learning performance of support vector machine classification (SVMC) algorithm with Markov chain samples based on linear prediction models to the case of Gaussian kernel. We present the numerical studies on the learning performance of Gaussian kernel SVMC algorithm based on Markov chain samples for benchmark repository.