Using an ensemble of classi ers, instead of a single classi er, can lead to improved generalization. The gains obtained by combining however, are often a ected more by the selection of what is presented to the combiner, than by the actual combining method that is chosen. In this paper we focus on data selection and classi er training methods, in order to \prepare" classi ers for combining. We r...
