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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:2078-2489
2078-2489
DOI:10.3390/info15050270