A new algorithm for support vector regression with automatic selection of hyperparameters

•Establishing an extended primal objective function based on probability regularization leading to a data dependent proportion parameter ν and ϵ.•This regularization establishes the equivalence of the ϵ-SVR and ν-SVR in which ν is specified as a proportion of ϵ−1log(1+ϵ).•It proposes values that are equivalent to the maximum likelihood estimates under the distributional assumptions by the loss function; ν is an explicit function of ϵ.