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  • 标题:Progress of machine learning in geosciences: Preface
  • 本地全文:下载
  • 作者:Amir H. Alavi ; Amir H. Alavi ; Amir H. Gandomi
  • 期刊名称:Geoscience Frontiers
  • 印刷版ISSN:1674-9871
  • 出版年度:2016
  • 卷号:7
  • 期号:1
  • 页码:1-2
  • DOI:10.1016/j.gsf.2015.10.006
  • 语种:English
  • 出版社:Elsevier
  • 摘要:In the past two decades, artificial intelligence (AI) algorithms have proved to be promising tools for solving several tough scien- tific problems. As a broad subfield of AI, machine learning is con- cerned with algorithms and techniques that allow computers to “learn”. The machine learning approach covers main domains such as data mining, difficult-to-program applications, and soft- ware applications. It is a collection of a variety of algorithms that can provide multivariate, nonlinear, nonparametric regression or classification. The remarkable simulation capabilities of the ma- chine learning-based methods have resulted in their extensive ap- plications in science and engineering. Recently, the machine learning techniques have found many applications in the geoscien- ces and remote sensing. More specifically, these techniques are proved to be practical for cases where the system’s deterministic model is computationally expensive or there is no deterministic model to solve the problem (Lary, 2010).
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