Convergence and Rates of Convergence of Recursive Radial Basis Functions Networks in Function Learning and Classification

  • Adam KrzyżakEmail author
  • Marian Partyka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10245)


In this paper we consider convergence and rates of convergence of the normalized recursive radial basis function networks in function learning and classification when network parameters are learned by the empirical risk minimization.


Nonlinear regression Classification Recursive radial basis function networks MISE convergence Strong convergence Rates of convergence 


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Software EngineeringConcordia UniversityMontrealCanada
  2. 2.Department of Knowledge Engineering, Faculty of Production Engineering and LogisticsOpole University of TechnologyOpolePoland

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