Approaches to decision-making under uncertainty in the belief function framework are
reviewed. Most methods are shown to blend criteria for decision under ignorance with the …

We demonstrate that numerous machine learning algorithms are specific instances of one
single paradigm: reciprocal learning. These instances range from active learning over multi …

Abstract Spaces with locally varying scale of measurement, like multidimensional structures
with differently scaled dimensions, are pretty common in statistics and machine learning …

Self-training is a simple yet effective method within semi-supervised learning. Self-training's
rationale is to iteratively enhance training data by adding pseudo-labeled data. Its …

Although being a crucial question for the development of machine learning algorithms, there
is still no consensus on how to compare classifiers over multiple data sets with respect to …

We propose a framework for descriptively analyzing sets of partial orders based on the
concept of depth functions. Despite intensive studies of depth functions in linear and metric …

To select strategic partners for local tax departments to provide tax information systems
(TISs) and long-term and quality services, which is generally modeled as a multiple criteria …

In probabilistic analyses and structural reliability assessments, it is often difficult or infeasible
to reliably identify the proper probabilistic models for the uncertain variables due to limited …

Learning human objectives from preference feedback has significantly advanced
reinforcement learning (RL) in domains with hard-to-formalize objectives. Traditional …

We consider a decision making problem under imprecision, where the probabilistic
information is given in terms of a set of probability measures, and where finding the optimal …