nips nips2006 nips2006-115 nips2006-115-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, Joshua B. Tenenbaum
Abstract: 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.
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