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Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression
School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, China
School of Accounting, Anhui University of Finance and Economics, Bengbu, China
School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu, China
- 1 School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, China
- 2 School of Accounting, Anhui University of Finance and Economics, Bengbu, China
- 3 School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu, China
Applied Mathematics·Volume 15 (2024)·Pages 51–64·Published 18 January 2024·DOI10.4236/am.2024.151006
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Abstract
In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.
KeywordsGlass CompositionL1 Regularization Logistic Regression ModelK-Means Clustering AnalysisElbow RuleParameter Verification
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