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Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks

scholarly article by Julian D. Olden & Donald A Jackson published August 2002 in Ecological Modelling

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Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks (English)

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artificial neural network

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Donald A Jackson

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Julian D. Olden

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Julian D Olden

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publication date

August 2002

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number of pages

16

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Ecological Modelling

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volume

154

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issue

1-2

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page(s)

135-150

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The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake

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Abundance, diversity, and structure of freshwater invertebrates and fish communities: An artificial neural network approach

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The potential for computer-aided identification in biodiversity research

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Energy availability and habitat heterogeneity predict global riverine fish diversity

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Multilayer feedforward networks are universal approximators

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Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird

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An artificial neural network approach for studying phytoplankton succession

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Torturing data for the sake of generality: How valid are our regression models?

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Fish–Habitat Relationships in Lakes: Gaining Predictive and Explanatory Insight by Using Artificial Neural Networks

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Learning representations by back-propagating errors

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Learning in Artificial Neural Networks: A Statistical Perspective

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Stacked generalization

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10.1016/S0304-3800(02)00064-9

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Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks

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