… combined with advanced machine learning methods. Furthermore, … machine learning
methods are of central importance. This paper gives an overview over the use of machine learning …

… analyze the convergence of gamification and machine learning, with a rigor focus of its
application in learning environments, and to investigate the effect of this convergence over the …

… of qualitative analysis and machine learning as very different domains (… machine learning
(ML) and grounded theory method (GTM), with a focus on setting up our points of convergence. …

… With the advent of Machine Learning (ML), recent advancements in intelligent techniques …
This survey is targeted towards a comprehensive study of the convergence of ML and …

… For instance, this convergence system, which relies on machine learning skills and … the
convergence of IoT with blockchain and machine learning algorithms, as well as the convergence …

… the dynamic aspects of multi-technology convergence. This study proposes a machine
learning approach to anticipating multi-technology convergence using patent information. For this…

… found for predicting converged cutoff and k-points during correlation study, we chose to use
machine learning (ML) to predict these quantities. We train machine learning models for cut-…

… many signal processing and machine learning tasks where the … robust performance and
fast convergence. We provide explicit … to decentralized training of machine learning models. …

… of machine learning and federated learning algorithms. In … classifier models of machine
learning and federated learning. In … and compare machine learning and federated learning. …

… is guaranteed to converge to a local maximum when an appropriate learning rate schedule
is used. Whether this can be used in practice is not clear, as too conservative learning rate …