How to measure uncertainty in uncertainty sampling for active learning

VL Nguyen, MH Shaker, E Hüllermeier - Machine Learning, 2022 - Springer
Various strategies for active learning have been proposed in the machine learning literature.
In uncertainty sampling, which is among the most popular approaches, the active learner …

Uncertainty measures: A critical survey

F Cuzzolin - Information Fusion, 2024 - Elsevier
Classical probability is not the only mathematical theory of uncertainty, or the most general.
Many authors have argued that probability theory is ill-equipped to model the 'epistemic' …

[图书][B] Possibilistic reasoning with imprecise probabilities: statistical inference and dynamic filtering

D Hose - 2022 - dominikhose.github.io
Die vorliegende Dissertation entstand während meiner Zeit als wissenschaftlicher
Mitarbeiter am Institut für Technische und Numerische Mechanik (ITM) der Universität …

Epistemic deep learning

SK Manchingal, F Cuzzolin - arXiv preprint arXiv:2206.07609, 2022 - arxiv.org
The belief function approach to uncertainty quantification as proposed in the Demspter-
Shafer theory of evidence is established upon the general mathematical models for set …

Random-set convolutional neural network (rs-cnn) for epistemic deep learning

SK Manchingal, M Mubashar, K Wang… - arXiv preprint arXiv …, 2023 - arxiv.org
Machine learning is increasingly deployed in safety-critical domains where robustness
against adversarial attacks is crucial and erroneous predictions could lead to potentially …

[HTML][HTML] Robust classification of multivariate time series by imprecise hidden Markov models

A Antonucci, R De Rosa, A Giusti, F Cuzzolin - International Journal of …, 2015 - Elsevier
A novel technique to classify time series with imprecise hidden Markov models is presented.
The learning of these models is achieved by coupling the EM algorithm with the imprecise …

Bayesian networks with imprecise probabilities: Theory and application to classification

G Corani, A Antonucci, M Zaffalon - Data Mining: Foundations and …, 2012 - Springer
Bayesian networks are powerful probabilistic graphical models for modelling uncertainty.
Among others, classification represents an important application: some of the most used …

Pari-mutuel probabilities as an uncertainty model

I Montes, E Miranda, S Destercke - Information Sciences, 2019 - Elsevier
The pari-mutuel model is a betting scheme that has its origins in horse racing, and that has
been applied in a number of contexts, mostly economics. In this paper, we consider the set …

Visions of a generalized probability theory

F Cuzzolin - arXiv preprint arXiv:1810.10341, 2018 - arxiv.org
In this Book we argue that the fruitful interaction of computer vision and belief calculus is
capable of stimulating significant advances in both fields. From a methodological point of …

Pseudo credal networks for inference with probability intervals

HD Estrada-Lugo, S Tolo… - … -ASME Journal of …, 2019 - asmedigitalcollection.asme.org
The computation of the inference corresponds to an NP-hard problem even for a single
connected credal network. The novel concept of pseudo networks is proposed as an …