Semi-Supervised Learning with Trees

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.

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Charles Kemp, Thomas L. Griffiths, Sean Stromsten, Joshua B. Tenenbaum
• Keywords: NIPS 2003, NIPS 2007, Nonparametric Bayesian Approach, Optimal Bayesian Classification, Unlabeled Objects