A Comparative Analysis of the Bayesian Regularization and Levenberg–Marquardt Training Algorithms in Neural Networks for Small Datasets: A Metrics Prediction of Neolithic Laminar Artefacts

This study aims to present a comparative analysis of the Bayesian regularization backpropagation and Levenberg–Marquardt training algorithms in neural networks for the metrics prediction of damaged archaeological artifacts, of which the state of conservation is often fragmented due to different reas...

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Published in:	Information (Basel) Vol. 15; no. 5; p. 270
Main Authors:	Troiano, Maurizio, Nobile, Eugenio, Mangini, Fabio, Mastrogiuseppe, Marco, Conati Barbaro, Cecilia, Frezza, Fabrizio
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.05.2024
Subjects:	Algorithms archaeological data Archaeology Artificial intelligence Back propagation Back propagation networks Bayesian analysis Bayesian regularization Comparative analysis Datasets Historic artifacts Levenberg–Marquardt Machine learning Mean square errors metrics prediction neural network Neural networks Pottery Protection and preservation Regularization Regularization methods Rites, ceremonies and celebrations Stone Age training algorithms United Kingdom
ISSN:	2078-2489, 2078-2489
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Abstract	This study aims to present a comparative analysis of the Bayesian regularization backpropagation and Levenberg–Marquardt training algorithms in neural networks for the metrics prediction of damaged archaeological artifacts, of which the state of conservation is often fragmented due to different reasons, such as ritual, use wear, or post-depositional processes. The archaeological artifacts, specifically laminar blanks (so-called blades), come from different sites located in the Southern Levant that belong to the Pre-Pottery B Neolithic (PPNB) (10,100/9500–400 cal B.P.). This paper shows the entire procedure of the analysis, from its normalization of the dataset to its comparative analysis and overfitting problem resolution.
AbstractList	This study aims to present a comparative analysis of the Bayesian regularization backpropagation and Levenberg–Marquardt training algorithms in neural networks for the metrics prediction of damaged archaeological artifacts, of which the state of conservation is often fragmented due to different reasons, such as ritual, use wear, or post-depositional processes. The archaeological artifacts, specifically laminar blanks (so-called blades), come from different sites located in the Southern Levant that belong to the Pre-Pottery B Neolithic (PPNB) (10,100/9500–400 cal B.P.). This paper shows the entire procedure of the analysis, from its normalization of the dataset to its comparative analysis and overfitting problem resolution.
Audience	Academic
Author	Mangini, Fabio Mastrogiuseppe, Marco Troiano, Maurizio Nobile, Eugenio Frezza, Fabrizio Conati Barbaro, Cecilia
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SubjectTerms	Algorithms archaeological data Archaeology Artificial intelligence Back propagation Back propagation networks Bayesian analysis Bayesian regularization Comparative analysis Datasets Historic artifacts Levenberg–Marquardt Machine learning Mean square errors metrics prediction neural network Neural networks Pottery Protection and preservation Regularization Regularization methods Rites, ceremonies and celebrations Stone Age training algorithms
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Title	A Comparative Analysis of the Bayesian Regularization and Levenberg–Marquardt Training Algorithms in Neural Networks for Small Datasets: A Metrics Prediction of Neolithic Laminar Artefacts
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