We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure to the domain. The tree (or a distribution over trees) may be inferred using the unlabeled data. A prior over concepts generated by a mutation process on the inferred tree(s) allows efficient computation of the optimal Bayesian classification function from the labeled examples. We test our approach on eight real-world datasets.
['Charles C. Kemp', 'Thomas L. Griffiths', 'Sean Stromsten', 'Joshua B. Tenenbaum']
Semi-Supervised Learning with Trees