A kind of admissible support vector kernel called scaling kernels are presented in this paper.In fact

scaling kernels are the multi-dimensional scaling function with translation vectors and they are a set of complete bases in the sub-space of the square and integrable space.Hence

the goal of scaling kernel function support vector machines is to find the optimal scaling coefficients in a scaling space.In terms of theory

scaling kernel function SVMs can approximate any objective function in some space by any precision.The results obtained by our simulations show the feasibility and validity of scaling kernels.