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A Bayesian recurrent neural network for unsupervised pattern recognition in large incomplete data sets
Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Pharmacology. WHO Collaborating Centre for International Drug Monitoring, Uppsala Monitoring Centre (UMC), SE-753 20 Uppsala, Sweden.
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2005 (English)In: International Journal of Neural Systems, ISSN 0129-0657, E-ISSN 1793-6462, Vol. 15, no 3, 207-222 p.Article in journal (Refereed) PublishedText
Abstract [en]

A recurrent neural network, modified to handle highly incomplete training data is described. Unsupervised pattern recognition is demonstrated in the WHO database of adverse drug reactions. Comparison is made to a well established method, AutoClass, and the performances of both methods is investigated on simulated data. The neural network method performs comparably to AutoClass in simulated data, and better than AutoClass in real world data. With its better scaling properties, the neural network is a promising tool for unsupervised pattern recognition in huge databases of incomplete observations.

Place, publisher, year, edition, pages
Singapore: World Scientific, 2005. Vol. 15, no 3, 207-222 p.
Keyword [en]
unsupervised pattern recognition, clustering, Hopfield network, adverse drug reactions
National Category
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-120420DOI: 10.1142/S0129065705000219ISI: 000233460100004PubMedID: 16013091OAI: oai:DiVA.org:umu-120420DiVA: diva2:934332
Available from: 2016-06-08 Created: 2016-05-16 Last updated: 2016-06-08Bibliographically approved

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