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.
Original language | English |
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Title of host publication | Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000. IJCNN 2000 |
Place of Publication | Piscataway, NJ, United States |
Publisher | IEEE |
Pages | 455-460 |
Number of pages | 6 |
Volume | 4 |
ISBN (Print) | 9780769506197 |
DOIs | |
Publication status | Published - 13 Aug 2000 |
Event | International Joint Conference on Neural Networks - Como, Italy Duration: 24 Jul 2000 → 27 Jul 2000 |
Conference
Conference | International Joint Conference on Neural Networks |
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Abbreviated title | IJCNN 2000 |
Country/Territory | Italy |
City | Como |
Period | 24/07/00 → 27/07/00 |
Keywords
- Bayes methods
- learning
- artificial intelligence
- neural nets
- Bayesian training
- MDN error function
- mixture density networks
- R-propagation
- conditional probability density