2021
DOI: 10.1007/978-3-030-91608-4_57
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Developments on Support Vector Machines for Multiple-Expert Learning

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Cited by 2 publications
(1 citation statement)
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“…This application is different than conventional classification frameworks in the sense that labels of contaminated or not contaminated are generated based on multiple threshold values; thus, a single sample could have a vector of labels corresponding to each threshold value. This is referred to as multiexpert learning [ 117 ]. In this context, support vector machines (SVM) were used with a majority vote.…”
Section: Data Description and Analysismentioning
confidence: 99%
“…This application is different than conventional classification frameworks in the sense that labels of contaminated or not contaminated are generated based on multiple threshold values; thus, a single sample could have a vector of labels corresponding to each threshold value. This is referred to as multiexpert learning [ 117 ]. In this context, support vector machines (SVM) were used with a majority vote.…”
Section: Data Description and Analysismentioning
confidence: 99%