Title :
Regularisation of mixture density networks
Author :
Hjorth, Lars U. ; Nabney, Ian T.
Author_Institution :
Neural Comput. Res. Group, Aston Univ., Birmingham, UK
Abstract :
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
Keywords :
neural nets; Bayesian regularisation; maximum likelihood estimation; mixture density networks; multivalued functions; neural networks; probability;
Conference_Titel :
Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470)
Conference_Location :
Edinburgh
Print_ISBN :
0-85296-721-7
DOI :
10.1049/cp:19991162
