Structured machine learning: the next ten years

DOI10.1007/s10994-008-5079-1zbMath1470.68098OpenAlexW2107261164MaRDI QIDQ1009285

Thomas G. Dietterich, Pedro Domingos, Prasad Tadepalli, Lise Getoor, Stephen H. Muggleton

Publication date: 31 March 2009

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

Full work available at URL: https://doi.org/10.1007/s10994-008-5079-1

zbMATH Keywords

inductive logic programmingstatistical relational learningrelational learningstructured machine learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Logic programming (68N17) Future perspectives in mathematics (01A67)

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Uses Software

• IBAL
• darch
• NetKit
• PRISM
• GOLEM

Cites Work

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• Practical solution techniques for first-order MDPs
• Probabilistic Horn abduction and Bayesian networks
• Mathematical applications of inductive logic programming
• Application of abductive ILP to learning metabolic network inhibition from temporal data
• Sequential inference with reliable observations: Learning to construct force-dynamic models
• Markov logic networks
• Scene Labeling by Relaxation Operations
• Numerical Optimization
• Inductive Logic Programming: Theory and methods
• Unifying Logical and Statistical AI
• 10.1162/jmlr.2003.3.4-5.679
• Inductive Logic Programming
• First-Order Probabilistic Languages: Into the Unknown
• Algorithmic Learning Theory
• A Fast Learning Algorithm for Deep Belief Nets
• Inductive Logic Programming
• Inductive Logic Programming
• Relational reinforcement learning
• Parameter estimation in stochastic logic programs