publication . Other literature type . Article . 2014

Training of Artificial Neural Networks Using Information-Rich Data

Shailesh Singh; Sharad Jain; András Bárdossy;
Open Access English
  • Published: 22 Jul 2014 Journal: Hydrology (issn: 2306-5338, Copyright policy)
  • Publisher: Multidisciplinary Digital Publishing Institute
Abstract
Artificial Neural Networks (ANNs) are classified as a data-driven technique, which implies that their learning improves as more and more training data are presented. This observation is based on the premise that a longer time series of training samples will contain more events of different types, and hence, the generalization ability of the ANN will improve. However, a longer time series need not necessarily contain more information. If there is considerable repetition of the same type of information, the ANN may not become “wiser”, and one may be just wasting computational effort and time. This study assumes that there are segments in a long time series that co...
Subjects
free text keywords: data-driven modelling, artificial neural networks, training, depth function, information-rich data, critical events, Data set, Training set, Pattern recognition, Magnitude (mathematics), Sampling (statistics), Artificial intelligence, business.industry, business, Data mining, computer.software_genre, computer, Data series, Artificial neural network, Geology, Climatology
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