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

Abstract

Abstract We consider the moving least-squares (MLS) method by the regression learning framework under the assumption that the sampling process satisfies the α-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 α-mixing, we derive the satisfactory learning rate and error bound of the algorithm.

Keywords

• Moving least-squares
• Regression function
• Mixing sequence
• Probability inequality
• Error bound