| Автор | Wilkins, Malcolm F. |
| Автор | Morris, Colin |
| Автор | Boddy, Lynne |
| Дата выпуска | 1994 |
| dc.description | Two artifical neural network classifiers, the well-known Multi-layer Perceptron (MLP) (also known as the ‘backpropagation network’), and the more recently developed Radial Basis Function (RBF) network, were evaluated and compared for their ability to identify multivariate flow cytometric data from five North Sea plankton groups (Dinoflagellidae, Bacillariophyceae, Prymnesiomonadida, Cryptomonadida, and other flagellates). RBF networks generally performed similarly to MLPs , and slightly better in cases where the data were markedly multimodal; RBF networks also have much shorter training times. The performance of MLPs was improved greatly by the use of a symmetrical bipolar ‘transfer function’ as opposed to the commonly-used asymmetric form. The issues of network optimisation and computational efficiency in use are discussed. |
| Формат | application.pdf |
| Издатель | Oxford University Press |
| Копирайт | © Oxford University Press |
| Тема | ORIGINAL PAPERS |
| Название | A comparison of Radial Basis Function and backpropagation neural networks for identification of marine phytoplankton from multivariate flow cytometry data |
| Тип | research-article |
| Electronic ISSN | 1367-4811 |
| Print ISSN | 1367-4803 |
| Журнал | Bioinformatics |
| Том | 10 |
| Первая страница | 285 |
| Последняя страница | 294 |
| Аффилиация | School of Pure and Applied Biology, University of WalesCardiff CF1 3TL, UK |
| Аффилиация | Morris Colin; Department of Computer Studies, University of Glamorgan |
| Выпуск | 3 |
