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Darling et al., 2018 - Google Patents

Toward uncertainty quantification for supervised classification

Darling et al., 2018

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Document ID
9402537173851549057
Author
Darling M
Stracuzzi D
Publication year

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Our goal is to develop a general theoretical basis for quantifying uncertainty in supervised machine learning models. Current machine learning accuracy-based validation metrics indicate how well a classifier performs on a given data set as a whole. However, these …
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • G—PHYSICS
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