@other{webpublishedreference200811242870130885,
Title = {Kernel Regression by Mode Calculation of the Conditional Probability Distribution},
Author = {K\"{u}hn, S},
Year = {2008},
Abstract = {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.},
Url = {http://arxiv.org/abs/0811.3499}
}