Linked Life Data

10508067

Source:http://linkedlifedata.com/resource/pubmed/id/10508067

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
1999-11-1
pubmed:abstractText
We describe here the application of a type of artificial neural network, the Gaussian radial basis function (RBF) network, in the identification of a large number of phytoplankton strains from their 11-dimensional flow cytometric characteristics measured by the European Optical Plankton Analyser instrument. The effect of network parameters on optimization is examined. Optimized RBF networks recognized 34 species of marine and freshwater phytoplankton with 91. 5% success overall. The relative importance of each measured parameter in discriminating these data and the behavior of RBF networks in response to data from "novel" species (species not present in the training data) were analyzed.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0099-2240
pubmed:author
pubmed:issnType
Print
pubmed:volume
65
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
4404-10
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
Identification of phytoplankton from flow cytometry data by using radial basis function neural networks.
pubmed:affiliation
Cardiff School of Biosciences, University of Cardiff, Cardiff CF1 3TL, United Kingdom.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't