Least Ambiguous Set-Valued Classifiers With Bounded Error Levels

In most classification tasks, there are observations that are ambiguous and therefore difficult to correctly label. Set-valued classifiers output sets of plausible labels rather than a single label, thereby giving a more appropriate and informative treatment to the labeling of ambiguous instances. W...

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Vydané v:	Journal of the American Statistical Association Ročník 114; číslo 525; s. 223 - 234
Hlavní autori:	Sadinle, Mauricio, Lei, Jing, Wasserman, Larry
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Alexandria Taylor & Francis 02.01.2019 Taylor & Francis Group,LLC Taylor & Francis Ltd
Predmet:	Ambiguity Ambiguous observation Asymptotic properties Bayes classifier Classification Classifiers Conditional probability equations Estimators Labeling Labels Multiclass classification Nondeterministic classifier Oracle classifier Probability Property Regression analysis Reject option Statistical methods Statistics Theory and Methods
ISSN:	0162-1459, 1537-274X, 1537-274X
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