Initializing RBF-networks with small subsets of training examples

Miroslav Kubat, Martin Cooperson

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

3 Scopus citations

Abstract

An important research issue in RBF networks is how to determine the gaussian centers of the radial-basis functions. We investigate a technique that identifies these centers with carefully selected training examples, with the objective to minimize the network's size. The essence is to select three very small subsets rather than one larger subset whose size would exceed the size of the three small subsets unified. The subsets complement each other in the sense that when used by a nearest-neighbor classifier, each of them incurs errors in a different part of the instance space. The paper describes the example-selection algorithm and shows, experimentally, its merits in the design of RBF networks.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAAAI
Pages188-193
Number of pages6
ISBN (Print)0262511061
StatePublished - Jan 1 1999
Externally publishedYes
EventProceedings of the 1999 16th National Conference on Artificial Intelligence (AAAI-99), 11th Innovative Applications of Artificial Intelligence Conference (IAAI-99) - Orlando, FL, USA
Duration: Jul 18 1999Jul 22 1999

Publication series

NameProceedings of the National Conference on Artificial Intelligence

Other

OtherProceedings of the 1999 16th National Conference on Artificial Intelligence (AAAI-99), 11th Innovative Applications of Artificial Intelligence Conference (IAAI-99)
CityOrlando, FL, USA
Period7/18/997/22/99

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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