{"title":"Probabilistic Learning","authors":"T. Jo","doi":"10.1007/978-3-030-65900-4_6","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":47667,"journal":{"name":"Foundations and Trends in Machine Learning","volume":"4 1","pages":""},"PeriodicalIF":65.3000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Foundations and Trends in Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-030-65900-4_6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
期刊介绍：
Each issue of Foundations and Trends® in Machine Learning comprises a monograph of at least 50 pages written by research leaders in the field. We aim to publish monographs that provide an in-depth, self-contained treatment of topics where there have been significant new developments. Typically, this means that the monographs we publish will contain a significant level of mathematical detail (to describe the central methods and/or theory for the topic at hand), and will not eschew these details by simply pointing to existing references. Literature surveys and original research papers do not fall within these aims.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
