Self-organizing network for regression: Efficient implementation and comparative evaluation

Vladimir Cherkassky, Youngjun Lee, Hossein Lari-Najafi

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

14 Scopus citations

Abstract

A method called constrained topological mapping (CTM) has been recently proposed for nonparametric regression analysis (V. Cherkassky and H. Lari-NaJafi, 1990). The CTM algorithm is a modification of Kohonen self-organizing maps suitable for regression problems. The authors discuss efficient software implementations of the algorithm that may be especially attractive for multivariate problems which require a large number of units in a map. The authors present experimental comparisons with alternative neural network approaches (backpropagation) and conventional approaches (projection pursuit) to regression. These comparisons demonstrate overall superiority of the proposed CTM algorithm, both in terms of prediction and computational speed.

Original languageEnglish (US)
Title of host publicationProceedings. IJCNN-91-Seattle
Subtitle of host publicationInternational Joint Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Pages79-84
Number of pages6
ISBN (Print)0780301641
StatePublished - Dec 1 1991
EventInternational Joint Conference on Neural Networks - IJCNN-91-Seattle - Seattle, WA, USA
Duration: Jul 8 1991Jul 12 1991

Publication series

NameProceedings. IJCNN-91-Seattle: International Joint Conference on Neural Networks

Other

OtherInternational Joint Conference on Neural Networks - IJCNN-91-Seattle
CitySeattle, WA, USA
Period7/8/917/12/91

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