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Math. Comput. Appl. 2016, 21(2), 20; doi:10.3390/mca21020020

Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data

Department of Computer Engineering, Muş Alparslan University, 49100 Muş, Turkey
Academic Editor: Mehmet Pakdemirli
Received: 8 March 2016 / Revised: 18 May 2016 / Accepted: 19 May 2016 / Published: 24 May 2016
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Abstract

The objective of this study is to compare the predictive ability of Bayesian regularization with Levenberg–Marquardt Artificial Neural Networks. To examine the best architecture of neural networks, the model was tested with one-, two-, three-, four-, and five-neuron architectures, respectively. MATLAB (2011a) was used for analyzing the Bayesian regularization and Levenberg–Marquardt learning algorithms. It is concluded that the Bayesian regularization training algorithm shows better performance than the Levenberg–Marquardt algorithm. The advantage of a Bayesian regularization artificial neural network is its ability to reveal potentially complex relationships, meaning it can be used in quantitative studies to provide a robust model. View Full-Text
Keywords: Bayesian regularization; Levenberg–Marquardt; neural networks; training algorithms Bayesian regularization; Levenberg–Marquardt; neural networks; training algorithms
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kayri, M. Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data. Math. Comput. Appl. 2016, 21, 20.

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