EXAMPLES OF FLEXIBLE BAYESIAN REGRESSION AND CLASSIFICATION MODELS
BASED ON NEURAL NETWORKS AND GAUSSIAN PROCESSES

Neural networks and Gaussian processes can be used to to predict a
target value (or values) from a set of input values.  When the target
is real-valued, this is known as "regression".  When the target takes
on values from a finite set, it is known as "classification".
Simple examples of both sorts are discussed here.

Some more complex classification models for images are discussed in
Ex-image.doc.

The data and command files for these examples are in the "ex-netgp"
directory.