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• W1851781208 abstract "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." @default.
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• W1851781208 date "1999-10-01" @default.
• W1851781208 modified "2023-10-17" @default.
• W1851781208 title "Identification of Phytoplankton from Flow Cytometry Data by Using Radial Basis Function Neural Networks" @default.
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• W1851781208 doi "https://doi.org/10.1128/aem.65.10.4404-4410.1999" @default.
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