  We present a new approach, called meta-meta classification, to learning in
small-data settings. In this approach, one uses a large set of learning
problems to design an ensemble of learners, where each learner has high bias
and low variance and is skilled at solving a specific type of learning problem.
The meta-meta classifier learns how to examine a given learning problem and
combine the various learners to solve the problem. The meta-meta learning
approach is especially suited to solving few-shot learning tasks, as it is
easier to learn to classify a new learning problem with little data than it is
to apply a learning algorithm to a small data set. We evaluate the approach on
a one-shot, one-class-versus-all classification task and show that it is able
to outperform traditional meta-learning as well as ensembling approaches.
