Usage of the Kullback–Leibler divergence on posterior Dirichlet distributions to create a training dataset for a learning algorithm to classify driving behaviour events-Reference-Cited by-同舟云学术

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Usage of the Kullback–Leibler divergence on posterior Dirichlet distributions to create a training dataset for a learning algorithm to classify driving behaviour events

Published:2023-08 Issue: Volume:8 Page:100081
ISSN:2772-4158
Container-title:Journal of Computational Mathematics and Data Science
language:en
Short-container-title:Journal of Computational Mathematics and Data Science

Author:

Cesarini M.^ORCID,Brentegani E.,Ceci G.,Cerreta F.,Messina D.,Petrarca F.,Robutti M.

Publisher

Elsevier BV

Reference30 articles.

1. Rényi divergence and Kullback-Leibler divergence;Van Erven;IEEE Trans Inform Theory,2014

2. On information and sufficiency;Kullback;Ann Math Stat,1951

3. Model selection and multimodel inference: a practical information-theoretic approach;Burnham,2002

4. Random decision forests;Ho,1995

5. Random forests;Breiman;Mach Learn,2001

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