  Many classification problems focus on maximizing the performance only on the
samples with the highest relevance instead of all samples. As an example, we
can mention ranking problems, accuracy at the top or search engines where only
the top few queries matter. In our previous work, we derived a general
framework including several classes of these linear classification problems. In
this paper, we extend the framework to nonlinear classifiers. Utilizing a
similarity to SVM, we dualize the problems, add kernels and propose a
componentwise dual ascent method.
