  In this paper, we consider a class of nonlinear regression problems without
the assumption of being independent and identically distributed. We propose a
correspondent mini-max problem for nonlinear regression and give a numerical
algorithm. Such an algorithm can be applied in regression and machine learning
problems, and yield better results than traditional least square and machine
learning methods.
