Bayesian training of neural networks using genetic programming

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Bayesian neural networks trained using Markov chain Monte Carlo (MCMC) and genetic programming in binary space within Metropolis framework is proposed. It is tested and compared to classical MCMC method and is observed to give better results than classical approach.

Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3622-3626
Number of pages5
ISBN (Print)0780394909, 9780780394902
DOIs
Publication statusPublished - 2006
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: 16 Jul 200621 Jul 2006

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Conference

ConferenceInternational Joint Conference on Neural Networks 2006, IJCNN '06
Country/TerritoryCanada
CityVancouver, BC
Period16/07/0621/07/06

ASJC Scopus subject areas

  • Software

Fingerprint

Dive into the research topics of 'Bayesian training of neural networks using genetic programming'. Together they form a unique fingerprint.

Cite this