Bayesian training of mixture density networks

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

View graph of relations Save citation

Authors

Research units

Abstract

Mixture Density Networks (MDNs) are a well-established method for modelling the conditional probability density which is useful for complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we extend earlier research of a regularisation method for a special case of MDNs to the general case using evidence based regularisation and we show how the Hessian of the MDN error function can be evaluated using R-propagation. The method is tested on two data sets and compared with early stopping.

Details

Publication date13 Aug 2000
Publication titleProceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000. IJCNN 2000
Place of PublicationPiscataway, NJ, United States
PublisherIEEE
Pages455-460
Number of pages6
Volume4
ISBN (Print)9780769506197
Original languageEnglish
EventInternational Joint Conference on Neural Networks - Como, Italy

Conference

ConferenceInternational Joint Conference on Neural Networks
Abbreviated titleIJCNN 2000
CountryItaly
CityComo
Period24/07/0027/07/00

    Keywords

  • Bayes methods, learning, artificial intelligence, neural nets, Bayesian training, MDN error function, mixture density networks, R-propagation, conditional probability density

DOI

Employable Graduates; Exploitable Research

Copy the text from this field...