An introduction to learning algorithms and potential applications in geomorphometry and Earth surface dynamics
An introduction to learning algorithms and potential applications in geomorphometry and Earth surface dynamics
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DOI：
10.5194/esurf-4-445-2016
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发表时间：
2016-05
期刊：
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通讯作者：
A. Valentine;L. Kalnins
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文献类型：
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作者：
A. Valentine;L. Kalnins
抽象的。“学习算法”是一类计算工具，旨在从数据集中推断信息，然后预测性地应用该信息。它们特别适合于复杂的模式识别，或者需要对数学关系进行建模，但底层过程没有很好地理解，计算成本太高，或者信号被其他效应覆盖的情况。如果可以构造关系的示例的代表性集合，则学习算法可以同化其行为，并且然后可以用作其有效的近似计算实现。可以设想在地貌测量学和地球表面动力学方面的广泛应用，从地貌分类到给定输入力的侵蚀特征预测。在这里，我们提供了一个实际的概述，在这个一般框架内的各种方法，审查现有的使用地貌学和相关的应用，并讨论了一些因素，确定学习算法的方法是否适合任何给定的问题。
Abstract. “Learning algorithms” are a class of computational tool designed to infer information from a data set, and then apply that information predictively. They are particularly well suited to complex pattern recognition, or to situations where a mathematical relationship needs to be modelled but where the underlying processes are not well understood, are too expensive to compute, or where signals are over-printed by other effects. If a representative set of examples of the relationship can be constructed, a learning algorithm can assimilate its behaviour, and may then serve as an efficient, approximate computational implementation thereof. A wide range of applications in geomorphometry and Earth surface dynamics may be envisaged, ranging from classification of landforms through to prediction of erosion characteristics given input forces. Here, we provide a practical overview of the various approaches that lie within this general framework, review existing uses in geomorphology and related applications, and discuss some of the factors that determine whether a learning algorithm approach is suited to any given problem.