The Support Vector Regression Model: A new Improvement for some Data Reduction Methods with Application

Autor:	Moustafa Salem, Mohamed G. Khalil
Rok vydání:	2022
Předmět:	Statistics and Probability Modeling and Simulation Management Science and Operations Research Statistics Probability and Uncertainty
Zdroj:	Pakistan Journal of Statistics and Operation Research. :427-435
ISSN:	2220-5810 1816-2711
DOI:	10.18187/pjsor.v18i2.4049
Popis:	Support Vector Regression (SVR) formulates is an optimization problem to learn a regression function that maps from input predictor variables to output observed response values. The SVR is useful because it balances model complexity and prediction error, and it has good performance for handling high-dimensional data. In this paper, we use the SVR model to improve the principal component analysis and the factor analysis methods. Simulation experiments are performed to assessment the new method. Some useful applications to real data sets are presented for comparing the competitive SVR models. It is noted that with increasing sample size, the -SVR type under the principal component analysis is the best model. However, under the small sample sizes the SVR type under the factor analysis provided adequate results.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6e75059cda055c4d466bdc97ba68b9b9 https://doi.org/10.18187/pjsor.v18i2.4049 Zobrazit plný text záznamu