摘要：We study the asymptotical properties of indefinite kernel network with -norm regularization. The framework under investigation is different from classical kernel learning. Positive semidefiniteness is not required by the kernel function. By a new step stone technique, without any interior cone condition for input space and condition for the probability measure , satisfied error bounds and learning rates are deduced.
