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 |