Working with Jensen Shannon divergence part1 (Machine Learning)
Metrization of powers of the Jensen-Shannon divergence(arXiv) Author : Kazuki Okamura Abstract : Metrization of statistical divergences is useful in both theoretical and practical aspects. Considering the fractional powers of statistical divergences is one way to obtain metrics associated with divergences. With this motivation, Osán, Bussandri, and Lamberti (2018) considered…
Working with Jensen Shannon divergence part1 (Machine Learning) Metrization of powers of the Jensen-Shannon divergence(arXiv) Author : Kazuki Okamura Abstract : Metrization of statistical divergences is useful in both theoretical and practical aspects. Considering the fractional powers of statistical divergences is one way to obtain metrics associated with divergences. With this motivation, Osán, Bussandri, and Lamberti (2018) considered… deep-learning
Working Jensen Shannon divergence part1 Machine Learning Metrization power JensenShannon divergencearXiv Author Kazuki Okamura Abstract Metrization statistical divergence useful theoretical practical aspect Considering fractional power statistical divergence one way obtain metric associated divergence motivation Osán Bussandri Lamberti 2018 considered… deeplearning