Title :
Decision trees can initialize radial-basis function networks
Author_Institution :
Center for Adv. Comput. Studies, Univ. of Southwestern Louisiana, Lafayette, LA, USA
fDate :
9/1/1998 12:00:00 AM
Abstract :
Successful implementations of radial-basis function (RBF) networks for classification tasks must deal with architectural issues, the burden of irrelevant attributes, scaling, and some other problems. This paper addresses these issues by initializing RBF networks with decision trees that define relatively pure regions in the instance space; each of these regions then determines one basis function. The resulting network is compact, easy to induce, and has favorable classification accuracy
Keywords :
decision theory; feedforward neural nets; learning systems; pattern classification; trees (mathematics); decision trees; learning systems; neural nets; pattern classification; radial-basis function networks; scaling; Computer science; Concrete; Decision trees; Equations; Learning systems; Neural networks; Neurons; Pattern recognition; Radial basis function networks; Transforms;
Journal_Title :
Neural Networks, IEEE Transactions on
