Learning Bayesian networks with local structure
Learning in graphical models
ACM SIGKDD Explorations Newsletter
Learning Bayesian Networks
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
A comparison of approaches for learning probability trees
ECML'05 Proceedings of the 16th European conference on Machine Learning
Logical bayesian networks and their relation to other probabilistic logical models
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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Data that has a complex relational structure and in which observations are noisy or partially missing poses several challenges to traditional machine learning algorithms. One solution to this problem is the use of so-called probabilistic logical models (models that combine elements of first-order logic with probabilities) and corresponding learning algorithms. In this thesis we focus on directed probabilistic logical models. We show how to represent such models and develop several algorithms to learn such models from data.