Parallel and distributed systems for constructive neural network learning

J. Fletcher, Z. Obradovic

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

1 Scopus citations

Abstract

A constructive learning algorithm dynamically creates a problem-specific neural network architecture rather than learning on a pre-specified architecture. The authors propose a parallel version of their recently presented constructive neural network learning algorithm. Parallelization provides a computational speedup by a factor of O(t) where t is the number of training examples. Distributed and parallel implementations under p4 using a network of workstations and a Touchstone DELTA are examined. Experimental results indicate that algorithm parallelization may result not only in improved computational time, but also in better prediction quality.

Original languageEnglish (US)
Title of host publicationProceedings of the 2nd International Symposium on High Performance Distributed Computing, HPDC 1993
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages174-178
Number of pages5
ISBN (Electronic)0818639008
DOIs
StatePublished - 1993
Event2nd International Symposium on High Performance Distributed Computing, HPDC 1993 - Spokane, United States
Duration: Jul 20 1993Jul 23 1993

Publication series

NameProceedings of the IEEE International Symposium on High Performance Distributed Computing
ISSN (Print)1082-8907

Conference

Conference2nd International Symposium on High Performance Distributed Computing, HPDC 1993
Country/TerritoryUnited States
CitySpokane
Period7/20/937/23/93

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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