Rates of convergence for fast learning RBF neural nets

Research output: Chapter in Book/Report/Conference proceedingConference contribution


[Autom. eng. transl.] Neural networks are non-parametric regression models of interest for their effectiveness in the presence of a large number of explanatory variables. Among the various neural network schemes, the RBF type with rapid learning is characterized by an extremely simple estimation process. In this work the convergence speed of rapid learning RBF networks is made explicit by highlighting how, under tenuous regularity hypotheses, this does not depend on the number of regressors used.
Original languageEnglish
Title of host publicationXLII Riunione Scientifica Bari, 9-11 giugno 2004
Number of pages4
Publication statusPublished - 2004
EventXLII Riunione scientifica SIS - Bari
Duration: 9 Jun 200411 Jun 2004


ConferenceXLII Riunione scientifica SIS


  • Radial Basis Functions
  • neural networks
  • nonparametric regression


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