Rough set-based feature selection for weakly labeled data
Supervised learning is an important branch of machine learning (ML), which requires a
complete annotation (labeling) of the involved training data. This assumption is relaxed in …
complete annotation (labeling) of the involved training data. This assumption is relaxed in …
Learning from fuzzy labels: Theoretical issues and algorithmic solutions
A Campagner - International Journal of Approximate Reasoning, 2024 - Elsevier
In this article we study the problem of learning from fuzzy labels (LFL), a form of weakly
supervised learning in which the supervision target is not precisely specified but is instead …
supervised learning in which the supervision target is not precisely specified but is instead …
Learning from imprecise data: adjustments of optimistic and pessimistic variants
The problem of learning from imprecise data has recently attracted increasing attention, and
various methods to tackle this problem have been proposed. In this paper, we discuss and …
various methods to tackle this problem have been proposed. In this paper, we discuss and …
[HTML][HTML] A general framework for maximizing likelihood under incomplete data
Maximum likelihood is a standard approach to computing a probability distribution that best
fits a given dataset. However, when datasets are incomplete or contain imprecise data, a …
fits a given dataset. However, when datasets are incomplete or contain imprecise data, a …
A min-max regret approach to maximum likelihood inference under incomplete data
R Guillaume, D Dubois - International Journal of Approximate Reasoning, 2020 - Elsevier
Various methods have been proposed to express and solve maximum likelihood problems
with incomplete data. In some of these approaches, the idea is that incompleteness makes …
with incomplete data. In some of these approaches, the idea is that incompleteness makes …
Maximum likelihood estimation and coarse data
The term coarse data encompasses different types of incomplete data where the (partial)
information about the outcomes of a random experiment can be expressed in terms of …
information about the outcomes of a random experiment can be expressed in terms of …
Maximum likelihood with coarse data based on robust optimisation
This paper deals with the problem of probability estimation in the context of coarse data.
Probabilities are estimated using the maximum likelihood principle. Our approach …
Probabilities are estimated using the maximum likelihood principle. Our approach …
A maximum likelihood approach to inference under coarse data based on minimax regret
R Guillaume, D Dubois - Uncertainty Modelling in Data Science 9, 2019 - Springer
Various methods have been proposed to express and solve maximum likelihood problems
with incomplete data. In some of these approaches, the idea is that incompleteness makes …
with incomplete data. In some of these approaches, the idea is that incompleteness makes …
Belief revision and the EM algorithm
This paper provides a natural interpretation of the EM algorithm as a succession of revision
steps that try to find a probability distribution in a parametric family of models in agreement …
steps that try to find a probability distribution in a parametric family of models in agreement …
The AIM and EM algorithms for learning from coarse data
M Jaeger - Journal of Machine Learning Research, 2022 - jmlr.org
Statistical learning from incomplete data is typically performed under an assumption of
ignorability for the mechanism that causes missing values. Notably, the expectation …
ignorability for the mechanism that causes missing values. Notably, the expectation …