Roy, DM and Kemp, C and Mansinghka, VK and Tenenbaum, JB (2007) Learning annotated hierarchies from relational data. Advances in Neural Information Processing Systems. pp. 1185-1192. ISSN 1049-5258Full text not available from this repository.
The objects in many real-world domains can be organized into hierarchies, where each internal node picks out a category of objects. Given a collection of features and relations defined over a set of objects, an annotated hierarchy includes a specification of the categories that are most useful for describing each individual feature and relation. We define a generative model for annotated hierarchies and the features and relations that they describe, and develop a Markov chain Monte Carlo scheme for learning annotated hierarchies. We show that our model discovers interpretable structure in several real-world data sets.
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron job|
|Date Deposited:||04 Feb 2015 22:04|
|Last Modified:||05 Feb 2015 10:37|