BARTIN: a neural structure that learns to take Bayesian minimum risk decisions

摘要

BARTIN (Bayesian real time networks) is a general structure for learning Bayesian minimum risk (maximum expected utility) decision schemes. It can be realized in a great variety of forms. The features that distinguish it from a standard Bayesian minimum risk classifier are, (i) it implements a general method for incorporating a prior distribution, and (ii) its ability to learn a risk minimising decision scheme from training data. Included in the enumerative realization described later, and applicable to many other variants, is a method for proportionately biassing specific decisions. BARTIN provides a bridge between neural networks and classical taught decision classification methods that are less versatile but whose internal workings are often much clearer. It provides both the flexibility of a neural network and the structure and clarity of these more formal schemes.