Book excerpt: An alternative model to class conditional probability estimation for classification problems is introduced. The model assumes a deterministic mapping between input space and the set of classes, training data consists of probability distributions of the classes at the measurements points. A classification system is built by transforming the classification problem into regression problems, which are tackled by neural network experts using Bayesian methods. Numerical calculations on two public well-investigated real data sets are included.