Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1002/etc.5620130508 |
| Title (Primary) | Back-propagation neural networks-recognition vs. prediction capability |
| Author | Schüürmann, G.; Müller, E. |
| Journal | Environmental Toxicology and Chemistry |
| Year | 1994 |
| Department | OEC; COE |
| Volume | 13 |
| Issue | 5 |
| Page From | 743 |
| Page To | 747 |
| Language | englisch |
| Keywords | Neural network; Back-propagation; Prediction error; Leave-re-out procedure; Biodegradation |
| Abstract | Literature data on biodegradation kinetics of organic compounds, together with a descriptor representation, are subjected to a systematic analysis of the performance of back-propagation neural-network models. The results show distinct dependencies on various model parameters, particularly on the number of iteration cycles. The application of leave-n-out procedures leads to general recommendations for a proper evaluation of the recognition and prediction power of this class of nonlinear structure-activity models. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=24142 |
| Schüürmann, G., Müller, E. (1994): Back-propagation neural networks-recognition vs. prediction capability Environ. Toxicol. Chem. 13 (5), 743 - 747 |
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