M. Cesarini , E. Brentegani , G. Ceci , F. Cerreta , D. Messina , F. Petrarca , M. Robutti
{"title":"使用后验Dirichlet分布上的Kullback–Leibler散度为学习算法创建训练数据集，以对驾驶行为事件进行分类","authors":"M. Cesarini , E. Brentegani , G. Ceci , F. Cerreta , D. Messina , F. Petrarca , M. Robutti","doi":"10.1016/j.jcmds.2023.100081","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"8 ","pages":"Article 100081"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Usage of the Kullback–Leibler divergence on posterior Dirichlet distributions to create a training dataset for a learning algorithm to classify driving behaviour events\",\"authors\":\"M. Cesarini , E. Brentegani , G. Ceci , F. Cerreta , D. Messina , F. Petrarca , M. Robutti\",\"doi\":\"10.1016/j.jcmds.2023.100081\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":100768,\"journal\":{\"name\":\"Journal of Computational Mathematics and Data Science\",\"volume\":\"8 \",\"pages\":\"Article 100081\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational Mathematics and Data Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772415823000081\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Mathematics and Data Science","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772415823000081","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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 behaviour data.
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