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A Bayesian recurrent neural network for unsupervised pattern recognition in large incomplete data sets
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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) Published
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
2005. Vol. 15, no 3, 207-222 p.
Keyword [en]
unsupervised pattern recognition, clustering, Hopfield network, adverse drug reactions, neuroleptic malignant syndrome, dysphagia, therapy
Identifiers
URN: urn:nbn:se:kth:diva-15193ISI: 000233460100004Scopus ID: 2-s2.0-32944456867OAI: oai:DiVA.org:kth-15193DiVA: diva2:333234
Note
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2017-12-12Bibliographically approved

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Arnborg, Stefan
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Numerical Analysis and Computer Science, NADA
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