UNSUPERVISED LEARNING OF BAYESIAN NETWORKS VIA ESTIMATION OF DISTRIBUTION ALGORITHMS: AN APPLICATION TO GENE EXPRESSION DATA CLUSTERING

DOI10.1142/S0218488504002588zbMath1101.68784OpenAlexW2156896344WikidataQ62637452 ScholiaQ62637452MaRDI QIDQ5696978

Pedro Larrañaga, José A. Lozano, José-Maria Peña

Publication date: 19 October 2005

Published in: International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0218488504002588

zbMATH Keywords

Bayesian networksunsupervised learningestimation of distribution algorithmsgene expression data analysis

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

A review on evolutionary algorithms in Bayesian network learning and inference tasks ⋮ Scalability of using restricted Boltzmann machines for combinatorial optimization

Cites Work

• Wrappers for feature subset selection
• Estimating the dimension of a model
• Learning recursive Bayesian multinets for data clustering by means of constructive induction
• A survey of optimization by building and using probabilistic models
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: UNSUPERVISED LEARNING OF BAYESIAN NETWORKS VIA ESTIMATION OF DISTRIBUTION ALGORITHMS: AN APPLICATION TO GENE EXPRESSION DATA CLUSTERING