Learning Rates Of Least-square Regularized Regression With Polynomial Kernel On A N-dimensional Simplex

The target of this paper is the error analysis for the regression problem in learning theory. We present learning rates for the least-square regularized regression algorithms with polynomial kernels. The learning rates depend on the dimension of polynomial space and the polynomial reproducing kernel Hilbert space measured by covering numbers.