CatSight, a direct path to proper multi-variate time series change detection: perceiving a concept drift through common spatial pattern
A Flórez, I Rodríguez-Moreno, A Artetxe… - International Journal of …, 2023 - Springer
Detecting changes in data streams, with the data flowing continuously, is an important
problem which Industry 4.0 has to deal with. In industrial monitoring, the data distribution …
problem which Industry 4.0 has to deal with. In industrial monitoring, the data distribution …
Detection and identification method of drilling total hydrocarbon gas based on infrared spectroscopy and KL+ BP-rbf algorithm
H Liang, H Chen, J Guo, X Zuo - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In the process of deep oil and gas exploration, it is more and more difficult to identify
hydrocarbon gas in the reservoir bed. Conventional methods cannot overcome the …
hydrocarbon gas in the reservoir bed. Conventional methods cannot overcome the …
Recognizing motor imagery tasks from EEG oscillations through a novel ensemble-based neural network architecture
Brain-Computer Interfaces (BCI) provide effective tools aimed at recognizing different brain
activities, translate them into actions, and enable humans to directly communicate through …
activities, translate them into actions, and enable humans to directly communicate through …
[PDF][PDF] Improving an Ensemble of Neural Networks via a Novel Multi-class Decomposition Schema.
The need for high recognition performance demands increasingly complex machine
learning (ML) architectures, which might be extremely computationally burdensome to be …
learning (ML) architectures, which might be extremely computationally burdensome to be …
Binary Decomposition for Multi-Class Classification Problems: Development and Applications
P Li, H Liu - … International Conference on Machine Learning and …, 2023 - ieeexplore.ieee.org
Binary decomposition of a multi-class classification problem is a widely used method in the
field of machine learning, which involves using an ensemble of binary classifiers to …
field of machine learning, which involves using an ensemble of binary classifiers to …
Improving prediction accuracy using dynamic information
B Böken - 2022 - publishup.uni-potsdam.de
Accurately solving classification problems nowadays is likely to be the most relevant
machine learning task. Binary classification separating two classes only is algorithmically …
machine learning task. Binary classification separating two classes only is algorithmically …