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Graphical Jump Method for Neural Networks
Volume 15, Issue 4 (2017), pp. 669–690
Jing Chang   Herbert K. H. Lee  

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https://doi.org/10.6339/JDS.201710_15(4).00006
Pub. online: 4 August 2022      Type: Research Article      Open accessOpen Access

Published
4 August 2022

Abstract

A graphical tool for choosing the number of nodes for a neural network is introduced. The idea is to fit the neural network with a range of numbers of nodes at first, and then generate a jump plot using a transformation of the mean square errors of the resulting residuals. A theorem is proven to show that the jump plot will select several candidate numbers of nodes among which one is the true number of nodes. Then a single node only test, which has been theoretically justified, is used to rule out erroneous candidates. The method has a sound theoretical background, yields good results on simulated datasets, and shows wide applicability to datasets from real research.

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Keywords
Jump Plot Model Selection Neural Network

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