Generalized ordering-search for learning directed probabilistic logical models

Authors:
Jan Ramon;Tom Croonenborghs;Daan Fierens;Hendrik Blockeel;Maurice Bruynooghe
Affiliations:
Dept. of Computer Science, K.U. Leuven, Leuven, Belgium 3001;Dept. of Computer Science, K.U. Leuven, Leuven, Belgium 3001;Dept. of Computer Science, K.U. Leuven, Leuven, Belgium 3001;Dept. of Computer Science, K.U. Leuven, Leuven, Belgium 3001;Dept. of Computer Science, K.U. Leuven, Leuven, Belgium 3001
Venue:
Machine Learning
Year:
2008

Citing 10
Cited 5

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
Top-down induction of first-order logical decision trees

Artificial Intelligence
Blocks World revisited

Artificial Intelligence
A Machine Learning Approach to Building Domain-Specific Search Engines

IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning relational probability trees

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Dependency Networks for Relational Data

ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Markov logic networks

Machine Learning
Learning Bayesian Networks

Learning Bayesian Networks
Online learning and exploiting relational models in reinforcement learning

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Logical bayesian networks and their relation to other probabilistic logical models

ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming

Generalized Ordering-Search for Learning Directed Probabilistic Logical Models

Inductive Logic Programming
Learning directed probabilistic logical models from relational data

AI Communications
Learning directed probabilistic logical models: ordering-search versus structure-search

Annals of Mathematics and Artificial Intelligence
CP-logic theory inference with contextual variable elimination and comparison to BDD based inference methods

ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Learning directed relational models with recursive dependencies

ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently, there has been an increasing interest in directed probabilistic logical models and a variety of formalisms for describing such models has been proposed. Although many authors provide high-level arguments to show that in principle models in their formalism 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 is based on the ordering-search algorithm for Bayesian networks. We use relational probability trees as a representation for the CPDs. We present experiments on a genetics domain, blocks world domains and the Cora dataset.