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STORRE: Stirling Online Research Repository
http://hdl.handle.net/1893/7244| Appears in Collections: | Aquaculture Journal Articles |
| Peer Review Status: | Refereed |
| Title: | New methods for the analysis of binarized BIOLOG GN data of vibrio species: Minimization of stochastic complexity and cumulative classification |
| Author(s): | Gyllenberg, Mats Koski, Timo Dawyndt, Peter Lund, Tatu Thompson, Fabiano L Austin, Brian Swings, Jean |
| Contact Email: | brian.austin@stir.ac.uk |
| Keywords: | bacterial taxonomy machine learning cumulative classification |
| Issue Date: | Oct-2002 |
| Date Deposited: | 6-Aug-2012 |
| Citation: | Gyllenberg M, Koski T, Dawyndt P, Lund T, Thompson FL, Austin B & Swings J (2002) New methods for the analysis of binarized BIOLOG GN data of vibrio species: Minimization of stochastic complexity and cumulative classification. Systematic and Applied Microbiology, 25 (3), pp. 403-415. https://doi.org/10.1078/0723-2020-00109 |
| Abstract: | 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. |
| DOI Link: | 10.1078/0723-2020-00109 |
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| Licence URL(s): | http://www.rioxx.net/licenses/under-embargo-all-rights-reserved |
| File | Description | Size | Format | |
|---|---|---|---|---|
| austin_BIOLOGGN_2002.pdf | Fulltext - Published Version | 290.34 kB | Adobe PDF | Under Embargo until 3000-01-01 Request a copy |
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