May 15, 2020 · Our proposal concerns the statement of the optimisation problem within the framework of belief function.
In this paper, we consider such an issue where the classifier provides imprecise and/or uncertain predictions that need to be managed within the decision ...
This paper considers the optimisation of a mix of material pieces of different types in different containers where information about those pieces is ...
In this paper, we consider such an issue where the classifier provides imprecise and/or uncertain predictions that need to be managed within the decision ...
Handling Mixture Optimisation Problem Using Cautious Predictions and Belief Functions. https://doi.org/10.1007/978-3-030-50143-3_30.
Cautious classification aims to minimize errors by providing a reliable output based on an appropriate representation of uncertainty.
Missing: Optimisation | Show results with:Optimisation
The purpose of the paper is to report on such applications where the use of belief functions provides a convenient tool to handle 'messy' data problems.
May 31, 2022 · Recent works proposed to deal with this problem by using cautious classification techniques. This paper is in line with these works, espe ...
Missing: Optimisation | Show results with:Optimisation
We consider the problem of combining belief functions in a situation where pieces of evidence are held by agents at the node of a communication network.
Missing: Mixture Optimisation Predictions
Master Bayesian Optimization in Data Science to refine hyperparameters efficiently and enhance model performance with practical Python applications.