A hybrid random field model for scalable statistical learning

DOI10.1016/j.neunet.2009.06.017zbMath1335.68200OpenAlexW2027894354WikidataQ39963414 ScholiaQ39963414MaRDI QIDQ280358

Antonino Freno, Edmondo Trentin, Marco Gori

Publication date: 10 May 2016

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2009.06.017

zbMATH Keywords

Bayesian networksMarkov random fieldsprobabilistic graphical modelsstructure learninghybrid random fieldspseudo-likelihood estimation

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (1)

A hybrid random field model for scalable statistical learning

Uses Software

• L-BFGS
• UCI-ml
• TETRAD

Cites Work

• A hybrid random field model for scalable statistical learning
• On the limited memory BFGS method for large scale optimization
• Evidential reasoning using stochastic simulation of causal models
• Graphical models for associations between variables, some of which are qualitative and some quantitative
• A Bayesian method for the induction of probabilistic networks from data
• Estimating the dimension of a model
• On the optimality of the simple Bayesian classifier under zero-one loss
• Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks
• Learning Bayesian networks: The combination of knowledge and statistical data
• 10.1162/153244301753344614
• Markov Random Fields and Their Applications
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