title: Assessing rbf networks using DELVE.
abstract: In this paper, different methods for training radial basis function (RBF) networks for regression problems are described and illustrated. Then, using data from the DELVE archive, they are empirically compared with each other and with some other well known methods for machine learning. Each of the RBF methods performs well on at least one DELVE task, but none are as consistent as the best of the other non-RBF methods.
authors.affiliation: Division of Informatics, University of Edinburgh, Scotland, UK.
authors.forename: M
authors.initials: M
authors.lastname: Orr
authors.forename: J
authors.initials: J
authors.lastname: Hallam
authors.forename: A
authors.initials: A
authors.lastname: Murray
authors.forename: T
authors.initials: T
authors.lastname: Leonard
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016571:Neural Networks, Computer; D012044:Regression Analysis; D012984:Software / Q000592:standards