Machine learning in geosciences and remote sensing

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{Lary:2016:GSF,
  author =       "David J. Lary and Amir H. Alavi and 
                 Amir H. Gandomi and Annette L. Walker",
  title =        "Machine learning in geosciences and remote sensing",
  journal =      "Geoscience Frontiers",
  year =         "2016",
  volume =       "7",
  number =       "1",
  pages =        "3--10",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning, Geosciences, Remote sensing, Regression,
                 Classification",
  ISSN =         "1674-9871",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1674987115000821",
  DOI =          "doi:10.1016/j.gsf.2015.07.003",
  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