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Mostrando ítems 11-14 de 14
Feature selection with limited bit depth mutual information for portable embedded systems
(Elsevier, 2020-06)
[Abstract]: Since wearable computing systems have grown in importance in the last years, there is an increased interest in implementing machine learning algorithms with reduced precision parameters/computations. Not only ...
Insights into distributed feature ranking
(Elsevier, 2019)
[Abstract]: In an era in which the volume and complexity of datasets is continuously growing, feature selection techniques have become indispensable to extract useful information from huge amounts of data. However, existing ...
Dealing with heterogeneity in the context of distributed feature selection for classification
(Springer, 2021)
[Abstract]: Advances in the information technologies have greatly contributed to the advent of larger datasets. These datasets often come from distributed sites, but even so, their large size usually means they cannot be ...
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 ...