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This paper quantifies the approximation capability of radial basis function networks (RBFNs) and their applications in machine learning theory. The target is to deduce almost optimal rates of approximation and learning by RBFNs. For approximation, we show that for large classes of functions, the convergence rate of approximation by RBFNs is not slower than that of multivariate algebraic polynomials...
The generalization performance is the main concern of machine learning theoretical research. The previous main bounds describing the generalization ability of the Empirical Risk Minimization (ERM) algorithm are based on independent and identically distributed (i.i.d.) samples. In order to study the generalization performance of the ERM algorithm with dependent observations, we first establish the...
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