Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases

DOI10.1007/s10994-015-5481-4zbMath1346.68159OpenAlexW2052148895MaRDI QIDQ894696

Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik

Publication date: 2 December 2015

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-015-5481-4

zbMATH Keywords

missing dataMarkov logic networksstatistical relational learningexpectation maximization

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (7)

Approximate classification with web ontologies through evidential terminological trees and forests ⋮ Learning Distributional Programs for Relational Autocompletion ⋮ Online probabilistic theory revision from examples with ProPPR ⋮ Non-parametric learning of embeddings for relational data using Gaifman locality theorem ⋮ Transfer learning by mapping and revising boosted relational dependency networks ⋮ Scalable learning and inference in Markov logic networks ⋮ Lifted graphical models: a survey

Uses Software

• Alchemy

Cites Work

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• Greedy function approximation: A gradient boosting machine.
• Gradient-based boosting for statistical relational learning: the relational dependency network case
• Learning first-order probabilistic models with combining rules
• Top-down induction of first-order logical decision trees
• Markov logic networks
• Discriminative Structure Learning of Markov Logic Networks
• Markov Logic: An Interface Layer for Artificial Intelligence
• Structure learning of probabilistic logic programs by searching the clause space

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