This paper proposes a kernel classification algorithm(MDL2KC) based on the theory of maximum difference of
densities. MDL2KC not only ensure the estimate difference of densities fairly close to the true difference of densities, but
also maximize the difference of densities between two classes. As demonstrated by extensive experiments in artifical and
UCI datasets, the proposed algorithm has better classification effect and sparsity than the traditional ??2 kernel classification
algorithm.