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2006
Springer

Computational intelligence in earth sciences and environmental applications: Issues and challenges

10 years 2 months ago
Computational intelligence in earth sciences and environmental applications: Issues and challenges
This paper introduces a generic theoretical framework for predictive learning, and relates it to data-driven and learning applications in earth and environmental sciences. The issues of data quality, selection of the error function, incorporation of the predictive learning methods into the existing modeling frameworks, expert knowledge, model uncertainty, and other application-domain specific problems are discussed. A brief overview of the papers in the Special Issue is provided, followed by discussion of open issues and directions for future research. q 2006 Elsevier Ltd. All rights reserved.
Vladimir Cherkassky, Vladimir M. Krasnopolsky, Dim
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where NN
Authors Vladimir Cherkassky, Vladimir M. Krasnopolsky, Dimitri P. Solomatine, Julio J. Valdés
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