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2008
ACM

Generalized ordering-search for learning directed probabilistic logical models

8 years 2 months ago
Generalized ordering-search for learning directed probabilistic logical models
Abstract. Recently, there has been an increasing interest in directed probabilistic logical models and a variety of languages for describing such models has been proposed. Although many authors provide high-level arguments to show that in principle models in their language can be learned from data, most of the proposed learning algorithms have not yet been studied in detail. We introduce an algorithm, generalized ordering-search, to learn both structure and conditional probability distributions (CPDs) of directed probabilistic logical models. The algorithm upgrades the ordering-search algorithm for Bayesian networks. We use relational probability trees as a representation for the CPDs. We present experiments on blocks world domains, a gene domain and the Cora dataset.
Jan Ramon, Tom Croonenborghs, Daan Fierens, Hendri
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2008
Where ML
Authors Jan Ramon, Tom Croonenborghs, Daan Fierens, Hendrik Blockeel, Maurice Bruynooghe
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