Machine learning in geosciences and remote sensing
Machine learning in geosciences and remote sensing
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DOI：
10.1016/j.gsf.2015.07.003
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发表时间：
2016-01-01
影响因子：
8.9
通讯作者：
Walker, Annette L.
中科院分区：
文献类型：
--
作者：
Lary, David J.;Alavi, Amir H.;Walker, Annette L.
学习包含了广泛的复杂程序。机器学习（ML）是基于生物学习过程的人工智能的一个细分。ML方法处理从机器可读数据中学习的算法设计。ML涵盖了数据挖掘、难以编程的应用程序和软件应用程序等主要领域。它是各种算法的集合（例如神经网络，支持向量机，自组织映射，决策树，随机森林，基于案例的推理，遗传编程等）。它可以提供多变量、非线性、非参数回归或分类。基于ML的方法的建模能力导致了它们在科学和工程中的广泛应用。在这里，ML作为解决地球科学和遥感问题的有效方法的作用将得到强调。将概述一些ML技术的独特功能，特别关注遗传编程范式。此外，非参数回归和分类说明性的例子，以证明ML处理地球科学和遥感问题的效率。(C)2015年，中国地质大学（北京）和北京大学。制作和主办：Elsevier B.V.
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. (C) 2015, China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V.