Abstract
Mixture Density Networks (MDNs) are a well-established method for modeling 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 regularization method for a special case of MDNs to the general case using evidence based regularization 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.