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An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data
Ecological Modelling · 2004 · Vol. 178(3-4) · pp. 389–397
Julian D. Olden✉(Colorado State University)Michael Joy(Massey University)Russell G. Death(Massey University)
Species Distribution and Climate ChangeEcology and Vegetation Dynamics StudiesData Analysis with RArtificial neural networkComputer scienceVariable (mathematics)Similarity (geometry)Artificial intelligenceMachine learningBlack boxRaw dataData miningEcology
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References
Learning representations by back-propagating errors
Nature · 1986 · 30,045 citations
Neural networks for pattern recognition
Choice Reviews Online · 1994 · 18,690 citations
Application of neural networks to modelling nonlinear relationships in ecology
Ecological Modelling · 1996 · 710 citations
Review and comparison of methods to study the contribution of variables in artificial neural network models
Ecological Modelling · 2003 · 1,240 citations
Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks
Ecological Modelling · 2002 · 1,221 citations
Multilayer feedforward networks are universal approximators
Neural Networks · 1989 · 20,841 citations
Stopping Rules in Principal Components Analysis: A Comparison of Heuristical and Statistical Approaches
Ecology · 1993 · 2,273 citations
Multilayer feedforward networks are universal approximators
Neural Networks · 1989 · 9,346 citations
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