Listar por tema "Ensemble learning"
Mostrando ítems 1-5 de 5
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A One-Class Classification method based on Expanded Non-Convex Hulls
(Elsevier, 2023)[Abstract]: This paper presents an intuitive, robust and efficient One-Class Classification algorithm. The method developed is called OCENCH (One-class Classification via Expanded Non-Convex Hulls) and bases its operation ... -
Ensemble and continual federated learning for classification tasks
(Springer, 2023-09)[Abstract]: Federated learning is the state-of-the-art paradigm for training a learning model collaboratively across multiple distributed devices while ensuring data privacy. Under this framework, different algorithms have ... -
Ensembles for feature selection: A review and future trends
(Elsevier, 2019)[Abstract]: Ensemble learning is a prolific field in Machine Learning since it is based on the assumption that combining the output of multiple models is better than using a single model, and it usually provides good ... -
Information Fusion and Ensembles in Machine Learning
(2019)[Abstract] Traditionally, machine learning methods have used a single learning model to solve a particular problem. However, the idea of combining multiple models instead of a single one to solve a problem has its rationale ... -
On developing an automatic threshold applied to feature selection ensembles
(Elsevier, 2019-01)[Abstract]: Feature selection ensemble methods are a recent approach aiming at adding diversity in sets of selected features, improving performance and obtaining more robust and stable results. However, using an ensemble ...
