High-Low Level Support Vector Regression Prediction Approach (HL-SVR) for Data Modeling with Input Parameters of Unequal Sample Sizes

DOI10.1142/S0219876221500298MaRDI QIDQ3383759

No author found.

Publication date: 16 December 2021

Published in: International Journal of Computational Methods (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1906.05777

zbMATH Keywords

computer simulationSVRdental implantunequal sample sizes

Mathematics Subject Classification ID

Statistics (62-XX) Biology and other natural sciences (92-XX)

Cites Work

• Utilization of a least square support vector machine (LSSVM) for slope stability analysis
• Use of support vector regression in structural optimization: application to vehicle crashworthiness design
• Support vector regression based metamodeling for seismic reliability analysis of structures
• A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
• Multi-fidelity optimization via surrogate modelling
• Sliced space-filling designs
• ANALYSIS OF PROTEIN ACTIVITY DATA BY GAUSSIAN STOCHASTIC PROCESS MODELS
• A generalized Wilcoxon test for comparing arbitrarily singly-censored samples

This page was built for publication: High-Low Level Support Vector Regression Prediction Approach (HL-SVR) for Data Modeling with Input Parameters of Unequal Sample Sizes