The Alpha-Beta-Symetric Divergence and their Positive Definite Kernel
Methodology
2018-09-18 v2 Machine Learning
Abstract
In this article we study the field of Hilbertian metrics and positive definit (pd) kernels on probability measures, they have a real interest in kernel methods. Firstly we will make a study based on the Alpha-Beta-divergence to have a Hilbercan metric by proposing an improvement of this divergence by constructing it so that its is symmetrical the Alpha-Beta-Symmetric-divergence (ABS-divergence) and also do some studies on these properties but also propose the kernels associated with this divergence. Secondly we will do mumerical studies incorporating all proposed metrics/kernels into support vector machine (SVM). Finally we presented a algorithm for image classification by using our divergence.
Keywords
Cite
@article{arxiv.1803.00001,
title = {The Alpha-Beta-Symetric Divergence and their Positive Definite Kernel},
author = {Mactar Ndaw and Macoumba Ndour and Papa Ngom},
journal= {arXiv preprint arXiv:1803.00001},
year = {2018}
}
Comments
1o pages, 11 figures