  The most direct way to express arbitrary dependencies in datasets is to
estimate the joint distribution and to apply afterwards the argmax-function to
obtain the mode of the corresponding conditional distribution. This method is
in practice difficult, because it requires a global optimization of a
complicated function, the joint distribution by fixed input variables. This
article proposes a method for finding global maxima if the joint distribution
is modeled by a kernel density estimation. Some experiments show advantages and
shortcomings of the resulting regression method in comparison to the standard
Nadaraya-Watson regression technique, which approximates the optimum by the
expectation value.
