… 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-…

… 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 …

… 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 further experiments, we could slightly improve on this performance with an ensemble of
… match outcome prediction lies in the successful incorporation of domain knowledge into the …

… 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 …

… generalizable VQA method named Dynamic Ensemble of Expert-Knowledge (DEEK), a …
the expertknowledge from each source domain to achieve a generalizable ensemble prediction. …

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

… 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 …

… technique into the field of software defect prediction to the best of our knowledge, although
it has been effectively applied to other domains. Through boosting method we get the most …