The Alpha-Beta-Symetric Divergence and their Positive Definite Kernel
This work addresses kernel methods in machine learning, but it appears incremental as it builds on existing divergences without clear broad impact.
The authors tackled the problem of constructing Hilbertian metrics and positive definite kernels from probability measures by proposing the Alpha-Beta-Symmetric divergence, an improved symmetrical version of the Alpha-Beta-divergence, and applied it to SVM for image classification.
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.