  We construct $\bf genRBF$ kernel, which generalizes the classical Gaussian
RBF kernel to the case of incomplete data. We model the uncertainty contained
in missing attributes making use of data distribution and associate every point
with a conditional probability density function. This allows to embed
incomplete data into the function space and to define a kernel between two
missing data points based on scalar product in $L_2$. Experiments show that
introduced kernel applied to SVM classifier gives better results than other
state-of-the-art methods, especially in the case when large number of features
is missing. Moreover, it is easy to implement and can be used together with any
kernel approaches with no additional modifications.
