Classi cation using Bayesian Neural Nets

Recently, Bayesian methods have been proposed for neural networks to solve regression and classi cation problems. These methods claim to overcome some di culties encountered in the standard approach such as over tting. However, an implementation of the full Bayesian approach to neural networks as suggested in the literature applied to classi cation problems is not easy. In fact we are not aware of applications of the full approach to real world classi cation problems. In this paper we discuss how the Bayesian framework can improve the predictive performance of neural networks. We demonstrate the e ects of this approach by an implementation of the full Bayesian framework applied to three real world classi cation problems. We also discuss the idea of calibration to measure the predictive performance.