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

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

Information theory uses the Kullback–Leibler divergence to compare distributions. In this paper, we apply it to bayesian posterior distributions and we show how it can be used to train a machine learning algorithm as well. The data sample used in this study is an OCTOTelematics set of driving behavi...

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Veröffentlicht in:Journal of Computational Mathematics and Data Science 2023-08, Vol.8, p.100081, Article 100081
Hauptverfasser: Cesarini, M., Brentegani, E., Ceci, G., Cerreta, F., Messina, D., Petrarca, F., Robutti, M.
Format: Artikel
Sprache:eng
Schlagworte:
Bayesian inference
Distributions comparison
Kullback–Leibler divergence
Machine learning
Random forest
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Zusammenfassung:Information theory uses the Kullback–Leibler divergence to compare distributions. In this paper, we apply it to bayesian posterior distributions and we show how it can be used to train a machine learning algorithm as well. The data sample used in this study is an OCTOTelematics set of driving behaviour data.
ISSN:2772-4158
2772-4158
DOI:10.1016/j.jcmds.2023.100081