Abstract: When the distribution of the input noise is known, the optimal parameter choice for the loss function can help SVR enhance its robustness. R-loss function is a more general form of both quadratic loss function and Laplacian loss function. Therefore, resea

Key words: Support vector machines（SVM）, Support vector regression（SVR）, R-loss function, Simulations