Le bagging (bootstrap aggregating) a été proposé par Leo Breiman en 1994 pour améliorer la classification en combinant des classifications d'ensembles d'entraînement générés aléatoirement.
Bagging (Bootstrap aggregating) was proposed by Leo Breiman in 1994 to improve classification by combining classifications of randomly generated training sets.
Le bootstrap aggregating, également appelée bagging (de bootstrap aggregating), est un meta-algorithme d'apprentissage ensembliste conçu pour améliorer la stabilité et la précision des algorithmes d'apprentissage automatique.
Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression.
Le bootstrap aggregating est un cas particulier de l'approche d'apprentissage ensembliste.
Bagging is a special case of the model averaging approach.
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Synonyms and analogies of "bootstrap aggregating" in French
