Nonlinear classifiers for ranking problems based on kernelized SVM
Mácha, Václav, Lukáš Adam, Václav Šmídl · arXiv (Cornell University) · 2020
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.