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 regularization method for MDNs by an extension of the evidence procedure. The method is tested on two data sets and compared with early stopping.
|Title of host publication||IEE Conference Publication|
|Place of Publication||United Kingdom|
|Publisher||Institution of Engineering and Technology (IET)|
|Number of pages||6|
|Publication status||Published - 1999|