Semi-Supervised Learning with Trees
Abstract
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.
Cite
Text
Kemp et al. "Semi-Supervised Learning with Trees." Neural Information Processing Systems, 2003.Markdown
[Kemp et al. "Semi-Supervised Learning with Trees." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/kemp2003neurips-semisupervised/)BibTeX
@inproceedings{kemp2003neurips-semisupervised,
title = {{Semi-Supervised Learning with Trees}},
author = {Kemp, Charles and Griffiths, Thomas L. and Stromsten, Sean and Tenenbaum, Joshua B.},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {257-264},
url = {https://mlanthology.org/neurips/2003/kemp2003neurips-semisupervised/}
}