Date: Wed Jan 16 04:27:08 2008
Authors: Shane Legg, Jan Poland, and Thomas Zeugmann
Abstract. This paper provides a short discussion concerning the state of the art in Bayesian learning theory with an emphasis on performance guarantees. In the second part of the paper, we outline some negative results indicating that there is no hope for a general learning algorithm that is computable and implementable, but powerful enough to learn any computable data.
©Copyright 2008 Authors