Ensemble classification of the VF dataset with limited merging

Ensemble classification (the concurrent development of K independent classifications) is a common practice in Gibbs classification. In this paper, we describe an adaptation of R. Azencott's (1992) theorem on finite-time annealing, coupled with a limited merging protocol, which produces a final low-energy Gibbs classification of the Virtual Female (VF) data set.