Semi-Supervised Learning with Trees

10 years 5 months ago
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.
Charles Kemp, Thomas L. Griffiths, Sean Stromsten,
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NIPS
Authors Charles Kemp, Thomas L. Griffiths, Sean Stromsten, Joshua B. Tenenbaum
Comments (0)