  The insensitive parameter in support vector regression determines the set of
support vectors that greatly impacts the prediction. A data-driven approach is
proposed to determine an approximate value for this insensitive parameter by
minimizing a generalized loss function originating from the likelihood
principle. This data-driven support vector regression also statistically
standardizes samples using the scale of noises. Nonlinear and linear numerical
simulations with three types of noises ($\epsilon$-Laplacian distribution,
normal distribution, and uniform distribution), and in addition, five real
benchmark data sets, are used to test the capacity of the proposed method.
Based on all of the simulations and the five case studies, the proposed support
vector regression using a working likelihood, data-driven insensitive parameter
is superior and has lower computational costs.
