Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences

TL;DR: In this paper, a new methodology for the prediction of daily PM10 concentrations, in line with the regulatory framework introduced through the EU Directive 2008/50/EC, is introduced, based on the efficient utilisation of the data collected over short time intervals (hourly) rather than the daily values used to derive the daily regulatory threshold.

TL;DR: In this article, the mean monthly total ozone time series over Arosa, Switzerland was analyzed and two neural networks with sigmoid activation function were used to predict the peak total ozone period (February-May) concentrations.

TL;DR: The validation using clinical data of the proposed change-detection scheme has shown that the use of only healthy and non-progressing eyes to train the algorithm led to a high diagnostic accuracy for detecting glaucoma progression compared to other methods.

TL;DR: A new model of data preprocessing based on a novel distributed computing architecture focused on large-scale datasets such as UGR’16 is presented and the adequateness of decision tree algorithms for training a machine learning model is shown by using a large dataset when compared with a multilayer perceptron neural network.

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.

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.

TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.