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

• 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
• DOI: 10.3390/info15050270

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.