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In many applications, the pre-information on regression function is always unknown. Therefore, it is necessary to learn regression function by means of some valid tools. In this paper we investigate the regression problem in learning theory, i.e., convergence rate of regression learning algorithm with least square schemes in multi-dimensional polynomial space. Our main aim is to analyze the generalization...
In this paper, regression problem in learning theory is investigated by least square schemes in polynomial space. Results concerning the estimation of rate of convergence are derived. In particular, it is shown that for one variable smooth regression function, the estimation is able to achieve good rate of convergence. As a main tool in the study, the Jackson operator in approximation theory is used...
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