The application of Artificial Intelligence (AI) across a wide range of domains comes with both
high expectations of its benefits and dire predictions of misuse. While AI systems have …

In machine learning, the presumably best model is selected from a variety of model
candidates generated by testing different model types, hyperparameters, or feature subsets …

Abstract The application of Deep Neural Networks (DNNs) to a broad variety of tasks
demands methods for co** with the complex and opaque nature of these architectures …

In clinical practice, algorithmic predictions may seriously jeopardise patients' health and thus
are required to be validated by medical experts before a final clinical decision is met …

Labeling of datasets is an essential task for supervised and semi-supervised machine
learning. Model-based active learning and user-based interactive labeling are two …

Artificial intelligence (AI) has become the new technique for solving the most complicated
challenges related to big data, image recognition, object detection, and prediction issues …

To compare and select machine learning models, relying on performance measures alone
may not always be sufficient. This is particularly the case where different subsets, features …

Human-centered machine learning is becoming an emerging field aiming to enable domain
experts that do not necessarily have a data science background to make use of machine …

ABSTRACT Human Computer Interaction became omnipresent in recent years, especially
gesture recognition. Thanks to major advances in machine learning, gesture recognition has …

In multi-label classification, we do not only want to analyze individual data items but also the
relationships between the assigned labels. Employing different sources and algorithms, the …