  For many types of machine learning algorithms, one can compute the
statistically `optimal' way to select training data. In this paper, we review
how optimal data selection techniques have been used with feedforward neural
networks. We then show how the same principles may be used to select data for
two alternative, statistically-based learning architectures: mixtures of
Gaussians and locally weighted regression. While the techniques for neural
networks are computationally expensive and approximate, the techniques for
mixtures of Gaussians and locally weighted regression are both efficient and
accurate. Empirically, we observe that the optimality criterion sharply
decreases the number of training examples the learner needs in order to achieve
good performance.
