Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
type: Item
instance of: scholarly article
title: Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
author: Eyke Hüllermeier
author: Willem Waegeman
published in: Machine Learning
publication date: 2021-11-24T00:00:00Z
full work available at URL: https://arxiv.org/abs/1910.09457
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 7432810
zbMATH Keywords: uncertainty
zbMATH Keywords: probability
zbMATH Keywords: epistemic uncertainty
zbMATH Keywords: version space learning
zbMATH Keywords: Bayesian inference
zbMATH Keywords: calibration
zbMATH Keywords: ensembles
zbMATH Keywords: Gaussian processes
zbMATH Keywords: deep neural networks
zbMATH Keywords: likelihood-based methods
zbMATH Keywords: credal sets and classifiers
zbMATH Keywords: conformal prediction
zbMATH Keywords: set-valued prediction
zbMATH Keywords: generative models
describes a project that uses: EfficientNet
describes a project that uses: IsolationForest
MaRDI profile type: Publication
OpenAlex ID: W3134774296
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Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods (DOI)
DOI: 10.1007/S10994-021-05946-3