Convergence rate for the moving least-squares learning with dependent sampling

We consider the moving least-squares (MLS) method by the regression learning framework under the assumption that the sampling process satisfies the alpha-mixing condition. We conduct the rigorous error analysis by using the probability inequalities for the dependent samples in the error estimates. When the dependent samples satisfy an exponential alpha-mixing, we derive the satisfactory learning rate and error bound of the algorithm.