Rather than assuming any particular parametric relationship between the predictor and target variables, kernel regression assumes only that the value of the target vari-able is a smooth function of the value of the predictors.
given a new point in predictor space, the value of the target at that point can reasonably be estimated by the value of the targets at points that are nearby in the predictor space.