Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: The role of kernels is central to machine learning. Motivated by the importance of power law distributions in modeling, simulation and learning, in this paper, we propose a powerlaw generalization of the Gaussian kernel. This generalization is based on q-Gaussian distribution, which is a power-law distribution studied in context of nonextensive statistical mechanics. We prove that the proposed kernel is positive definite, and provide some insights regarding the corresponding Reproducing Kernel Hilbert Space (RKHS). We also study practical significance of qGaussian kernels in classification, regression and clustering, and present some simulation results.

On q-Gaussian kernel and its Reproducing Kernel Hilbert Space