… In this study, domain knowledge refers to the specific domain to which each course belongs.
It is an important factor as each curriculum guideline has a different composition of courses. …

… data, DG models rely on the diversity of source domain to learn generalizable knowledge. …
peaked value on the predicted class [28]. Then, we force the ensemble prediction of all source-…

… information assisted convolutional LSTM ensemble method for RUL prediction, where fault
… knowledge transfer neural network (FTNN) based on a domain-shared 1-D CNN and domain …

… In this study, a domain knowledge-integrated ELM ensemble (DK-iELME) model based on
… , ELM algorithm and domain knowledge is proposed for fatigue life prediction under mean …

… Moreover, to build the individual BN, we firstly extract features from the domain knowledge
—… combine the predicting results to get a final classification result. The ensemble BN learning …

… ensemble members relative to the deterministic forecast? Does it make a difference if we ask
how many ensemble … initial perturbations consistent with our knowledge of analysis error? …

… knowledge from visually diverse domains to learn improved predictions for target domains
… a set of source domains, Ds,∀s ∈ {1,2, ..., S}, whose knowledge is compactly represented in …

… ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework
for prediction … risk factor framework, and an ensemble classifier. Therefore, a component-…

… alignment by employing a novel clustering loss with a batch-wise prediction similarity …
knowledge present within and across the source domains, we leverage the principles of Ensemble …

… and target domains jointly, the Self-Ensemble is better … knowledge that we adapt domain
only relies on unlabeled target data. It demonstrates that we effectively decouple the domain …