Title :
Bayesian training 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 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
Keywords :
Bayes methods; learning (artificial intelligence); neural nets; Bayesian training; MDN error function; Mixture Density Networks; R-propagation; conditional probability density; Bayesian methods; Computer networks; Gaussian processes; Hysteresis; Integrated circuit modeling; Inverse problems; Kernel; Neural networks; Predictive models; Testing;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.860813
