Study of the robustness of a meta-algorithm for the estimation of parameters in Radial Basis Function Neural Networks design

Title:

Study of the robustness of a meta-algorithm for the estimation of parameters in Radial Basis Function Neural Networks design

Creator:

Parras-Gutierrez, Elisabet, del Jesus, Jose , Rivals, Victor M., and Merelo , Juan J.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:7a620730-8b2f-4db2-a3b0-b17e946d7135
uuid:7a620730-8b2f-4db2-a3b0-b17e946d7135

Subject:

RBFN, meta-algorithm, evolutionary algorithm, and parameter estimation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Radial Basis Function Networks (RBFNs) have shown their capability to be used in classification problems, and therefore many data mining algorithms have been developed to configure RBFNs. These algorithms need to be given a suitable set of parameters for every problem they face, thus methods to automatically search the values of these parameters are required. This paper shows the robustness of a meta-algorithm developed to automatically establish the parameters needed to design RBFNs. Results show that this new method can be effectively used, not only to obtain good models, but also to find a stable set of parameters, available to be used on many different problems.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2009 Volume:19 | Number:1

Harvested from:

CDK

Metadata only:

false