How to measure uncertainty in uncertainty sampling for active learning
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 …
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' …
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 …
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 …
Shafer theory of evidence is established upon the general mathematical models for set …
Random-set convolutional neural network (rs-cnn) for epistemic deep learning
Machine learning is increasingly deployed in safety-critical domains where robustness
against adversarial attacks is crucial and erroneous predictions could lead to potentially …
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 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 …
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
Bayesian networks are powerful probabilistic graphical models for modelling uncertainty.
Among others, classification represents an important application: some of the most used …
Among others, classification represents an important application: some of the most used …
Pari-mutuel probabilities as an uncertainty model
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 …
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 …
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 …
connected credal network. The novel concept of pseudo networks is proposed as an …