This article establishes a novel electroencephalograph (EEG)-based brain-computer interface
(BCI) system for ground vehicle control with potential application of mobility assistance to …

… a cluster decomposing based ensemble learning framework (… combination, the ensemble
learning was formulated as a … proposed for solving the ensemble learning problem. Main results…

… Classification results in Table I show that ensemble learning increases kappa value on
average across the 9 sUbjects. The power of SLDA stems from its ability to model temporal …

… from subbands, and ensemble learning-based classifiers for … Finally, the ensemble machine
learning approach is used for … Results revealed that the suggested ensemble learning …

… and ensemble model can also be performed classification and regression [15]. There are
several effective ensemble approaches. Three ensemble learning-based approaches are …

… of ensemble learning for fNIRS-BCIs is evaluated. For this, the random subspace method
takes charge of the core of the ensemble learning … learner and an ensemble of multiple weak …

… method with various existing passive ensemble learning algorithms: Bagging, Boosting,
and Random Subspace; and an active ensemble learning via linear discriminant analysis (LDA)-…

… using ensemble learning methods … Ensemble learning is the practice of combining several
classifiers to obtain a final result. Ensemble learning methods can increase machine learning …

… In this paper, we develop and compare ensemble learning models that are capable of …
IV, essentially from a machine learning prospective. Our proposed ensemble learning architecture…

… We also propose a new classification method, based on ensemble learning and randomness
for parallel processing, decreasing the false detection rate and increasing sensitivity. To …