On Measures of Uncertainty in Classification

Author:

Chlaily Saloua1ORCID,Ratha Debanshu1ORCID,Lozou Pigi2ORCID,Marinoni Andrea1ORCID

Affiliation:

1. Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway

2. Department of Electrical Engineering and Computer Science, National Technical University of Athens, Athens, Greece

Funder

Research Council of Norway

Visual Intelligence Centre for Research-based Innovation through the Research Council of Norway

Automatic Multisensor remote sensing for Sea Ice Characterization (AMUSIC) Framsenteret

NATALIE project through the European Union Horizon Europe Climate research and innovation program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Reference45 articles.

1. Weight uncertainty in neural network;blundell;Proc 32nd Int Conf Mach Learn Proc Mach Learn Res,0

2. Classification and regression by randomforest;liaw;R News,2002

3. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning;gal;Proc 33rd Int Conf Mach Learn Proc Mach Learn Res,0

4. Support vector machines for classification and regression;gunn;Proc Support Vector Mach,0

5. Evidential deep learning to quantify classification uncertainty;sensoy;Proc Adv in Neural Inf Process Syst,2018

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