Regularisation of mixture density networks
Regularisation of mixture density networks
- Author(s): L.U. Hjorth and I.T. Nabney
- DOI: 10.1049/cp:19991162
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- Author(s): L.U. Hjorth 1 and I.T. Nabney 1
- Conference: 9th International Conference on Artificial Neural Networks: ICANN '99
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Source:
9th International Conference on Artificial Neural Networks: ICANN '99,
1999
p.
521 – 526
Affiliations:
1: Neural Comput. Res. Group, Aston Univ., Birmingham, UK
- DOI: 10.1049/cp:19991162
- ISBN: 0 85296 721 7
- Location: Edinburgh, UK
- Conference date: 7-10 Sept. 1999
- Format: PDF
Mixture density networks (MDNs) are a well-established method for modelling complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we develop a Bayesian regularisation method for MDNs by an extension of the evidence procedure. The method is tested on two data sets and compared with early stopping.
Inspec keywords: Bayes methods; probability; maximum likelihood estimation; neural nets
Subjects: Neural nets; Other topics in statistics
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