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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.


Support Vector Regression Factor Analysis Kernel Functions Principal Component Analysis v -Support Vector Regression

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How to Cite
Salem, M., & Khalil , M. G. (2022). The Support Vector Regression Model: A new Improvement for some Data Reduction Methods with Application. Pakistan Journal of Statistics and Operation Research, 18(2), 427-435.


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