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Synthetic data for design and evaluation of binary classifiers in the context of Bayesian transfer learning.

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第一作者： Omar,Maddouri

第一单位： Department of Electrical and Computer Engineering, Texas A&M University, College Station TX 77843, USA.

作者： Omar,Maddouri ^[1] ; Xiaoning,Qian ^[2] ; Francis J,Alexander ^[3] ; Edward R,Dougherty ^[1] ; Byung-Jun,Yoon ^[2]

作者单位： Department of Electrical and Computer Engineering, Texas A&M University, College Station TX 77843, USA. ^[1] Department of Electrical and Computer Engineering, Texas A&M University, College Station TX 77843, USA.;Computational Science Initiative, Brookhaven National Laboratory, Upton NY 11973, USA. ^[2] Computational Science Initiative, Brookhaven National Laboratory, Upton NY 11973, USA. ^[3]

关键词 Bayesian transfer learning Binary classification Classifier design Error estimation

DOI 10.1016/j.dib.2022.108113

PMID 35434232

发布时间 2022-07-16

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Data in brief

2022年42卷

108113页

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Synthetic data for design and evaluation of binary classifiers in the context of Bayesian transfer learning.

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Synthetic data for design and evaluation of binary classifiers in the context of Bayesian transfer learning.

Data in brief

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