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New methods for the analysis of binarized BIOLOG GN data of vibrio species: Minimization of stochastic complexity and cumulative classification
Univ Turku, Dept Math, FIN-20014 Turku, Finland Linkoping Univ, Dept Math, S-58183 Linkoping, Sweden State Univ Ghent, Microbiol Lab, B-9000 Ghent, Belgium Heriot Watt Univ, Dept Biol Sci, Edinburgh EH14 4AS, Midlothian, Scotland.
Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
Univ Turku, Dept Math, FIN-20014 Turku, Finland Linkoping Univ, Dept Math, S-58183 Linkoping, Sweden State Univ Ghent, Microbiol Lab, B-9000 Ghent, Belgium Heriot Watt Univ, Dept Biol Sci, Edinburgh EH14 4AS, Midlothian, Scotland.
Univ Turku, Dept Math, FIN-20014 Turku, Finland Linkoping Univ, Dept Math, S-58183 Linkoping, Sweden State Univ Ghent, Microbiol Lab, B-9000 Ghent, Belgium Heriot Watt Univ, Dept Biol Sci, Edinburgh EH14 4AS, Midlothian, Scotland.
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2002 (English)In: Systematic and Applied Microbiology, ISSN 0723-2020, E-ISSN 1618-0984, Vol. 25, no 3, 403-415 p.Article in journal (Refereed) Published
Abstract [en]

We apply minimization of stochastic complexity and the closely related method of cumulative classification to analyse the extensively studied BIOLOG GN data of Vibrio spp. Minimization of stochastic complexity provides an objective tool of bacterial taxonomy as it produces classifications that are optimal from the point of view of information theory. We compare the outcome of our results with previously published classifications of the same data set. Our results both confirm earlier detected relationships between species and discover new ones.

Place, publisher, year, edition, pages
2002. Vol. 25, no 3, 403-415 p.
Keyword [en]
bacterial taxonomy, machine learning, cumulative classification
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Engineering and Technology
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URN: urn:nbn:se:liu:diva-48752OAI: oai:DiVA.org:liu-48752DiVA: diva2:269648
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-12

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Koski, Timo

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