… the hydrological ensemble prediction systems (… predictions come from different atmospheric
circulation models, including deterministic weather predictions and the ensemble prediction …

… knowledge, none of these papers has predicted the performance degradation of aircraft
engines using ensemble … metric that measures the accuracy of the ensemble prediction; w s = [w …

… adopt the prediction averaging strategy to aggregate classifiers. Compared to existing
prediction … This work uses knowledge distillation that transfers the knowledge learned by the pre-…

… Approximate nonlinear systems effectively; not need domain knowledge Parameter …
After training, the trained RF-based ELM ensemble model can be used to predict the future …

… This question is not Straightforward; as already noted, our knowledge of analysis error is
poor. Moreover, it may not necessarily be the case that analysis-error structures with the largest …

… [19] described a data fusion approach where domain knowledge and … ensemble predicted
RUL for the testing data set y t ; M denotes the number of algorithm members in the ensemble…

… ensemble prediction model using the following weighted average aggregating rule: (10) M =
∑ i = 1 N v i M i , where M is the ensemble … is extracted using domain knowledge. Examples …

… In the proposed iDeep model, we integrated 5 sources of data for an ensemble prediction.
It will be interesting to see how the 5 independent modalities will complement with each other. …

… Feature generation for traditional default prediction mainly relies on expert domain
knowledge and it requires a significant cost of time for review. This condition makes it difficult for …

… out all this domain knowledge, and instead wanted to reuse such pre-existing knowledge to
create … This experience was instrumental in our formulation of the cluster ensemble problem. …