Journal Article10.1016/S1352-2310(97)00447-0
Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
M.W. Gardner,Stephen Dorling +1 more
3.2K
TL;DR: This paper presents a general introduction and discussion of recent applications of the multilayer perceptron, one type of artificial neural network, in the atmospheric sciences.
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About: This article is published in Atmospheric Environment. The article was published on 01 Aug 1998. The article focuses on the topics: Multilayer perceptron & Artificial neural network.
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Citations
Normalisation of imprecise temporal expressions extracted from text
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Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies
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TL;DR: In this article , the authors conducted a scoping review in PubMed and Web of Science on the applications of AI to measure, predict, and treat obesity and identified 46 studies that used diverse ML and DL models to assess obesity-related outcomes.
TE-Based Machine Learning Techniques for Link Fault Localization in Complex Networks
Srinikethan Madapuzi Srinivasan,Tram Truong-Huu,Mohan Gurusamy +2 more
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References
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
23.1K
•Book
Classification and regression trees
Leo Breiman
- 01 Jan 1983
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
22.7K
•Book
Neural networks for pattern recognition
Christopher M. Bishop
- 01 Jan 1995
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Learning internal representations by error propagation
David E. Rumelhart,Geoffrey E. Hinton,Ronald J. Williams +2 more
- 01 Jan 1988
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.