    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".
    Examples of both sorts are discussed here.

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