Semi-Supervised Learning with Trees

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Charles Kemp, Thomas Griffiths, Sean Stromsten, Joshua Tenenbaum


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 func- tion from the labeled examples. We test our approach on eight real-world datasets.