Machine learning in geosciences and remote sensing
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Lary:2016:GSF,
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author = "David J. Lary and Amir H. Alavi and
Amir H. Gandomi and Annette L. Walker",
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title = "Machine learning in geosciences and remote sensing",
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journal = "Geoscience Frontiers",
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year = "2016",
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volume = "7",
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number = "1",
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pages = "3--10",
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month = jan,
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keywords = "genetic algorithms, genetic programming, Machine
learning, Geosciences, Remote sensing, Regression,
Classification",
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ISSN = "1674-9871",
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URL = "http://www.sciencedirect.com/science/article/pii/S1674987115000821",
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DOI = "doi:10.1016/j.gsf.2015.07.003",
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abstract = "Learning incorporates a broad range of complex
procedures. Machine learning (ML) is a subdivision of
artificial intelligence based on the biological
learning process. The ML approach deals with the design
of algorithms to learn from machine readable data. ML
covers main domains such as data mining,
difficult-to-program applications, and software
applications. It is a collection of a variety of
algorithms (e.g. neural networks, support vector
machines, self-organizing map, decision trees, random
forests, case-based reasoning, genetic programming,
etc.) that can provide multivariate, nonlinear,
nonparametric regression or classification. The
modeling capabilities of the ML-based methods have
resulted in their extensive applications in science and
engineering. Herein, the role of ML as an effective
approach for solving problems in geosciences and remote
sensing will be highlighted. The unique features of
some of the ML techniques will be outlined with a
specific attention to genetic programming paradigm.
Furthermore, nonparametric regression and
classification illustrative examples are presented to
demonstrate the efficiency of ML for tackling the
geosciences and remote sensing problems.",
- }
Genetic Programming entries for
David John Lary
A H Alavi
A H Gandomi
Annette L Walker
Citations