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Back‐propagation neural networks‐recognition vs. prediction capability
Author(s) -
Schüürmann Gerrit,
Muller Eckhard
Publication year - 1994
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.5620130508
Subject(s) - artificial neural network , representation (politics) , nonlinear system , computer science , class (philosophy) , artificial intelligence , machine learning , data mining , backpropagation , pattern recognition (psychology) , biological system , physics , quantum mechanics , politics , political science , law , biology
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.