Sparse $$\varepsilon $$ ε -tube support vector regression by active learning

Fixed Parameters Support Vector Regression for Outlier Detection

“…Five points (1)(2)(3)(4)(5) were replaced by arbitrary large numbers equal to 35 to be five artificially bad leverage points (outlying in X and Y directions). In Figures 7, 8 and Table 4, we can see that the proposed method clearly succeeded to detect outliers for rank-deficient data.…”

Robust Support Vector Regression Model in the Presence of Outliers and Leverage Points

“…It is a universal technique to handle regression problems. Since then, SVR has attracted the interest of researchers due to its excellent performance of solving a variety of learning problems (Ceperic, 2014;Dhhan et al 2015). Some additional reasonsstand of behind the widely use of the SVR such as lower sensitivity to local minima, theoretical guarantees about its performance, and high flexibility to add extra dimensions to the input space, whichprevents the increasing of the model complexity (Ceperic, 2014).…”