« AODE » : différence entre les versions
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== Définition == | == Définition == | ||
Consideré comme l’un des algorithmes representatifs les plus intéressants parmi les classifieurs bayésiens | Consideré comme l’un des algorithmes representatifs les plus intéressants parmi les classifieurs bayésiens | ||
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''' AODE''' | ''' AODE''' | ||
''' Averaged one-dependence estimators ''' | '''Averaged one-dependence estimators''' | ||
Averaged one-dependence estimators (AODE) is a probabilistic classification learning technique. It was developed to address the attribute-independence problem of the popular naive Bayes classifier. It frequently develops substantially more accurate classifiers than naive Bayes at the cost of a modest increase in the amount of computation.[1] | Averaged one-dependence estimators (AODE) is a probabilistic classification learning technique. It was developed to address the attribute-independence problem of the popular naive Bayes classifier. It frequently develops substantially more accurate classifiers than naive Bayes at the cost of a modest increase in the amount of computation.[1] | ||
Version du 9 février 2022 à 16:45
Définition
Consideré comme l’un des algorithmes representatifs les plus intéressants parmi les classifieurs bayésiens
Français
AODE
Anglais
AODE
Averaged one-dependence estimators
Averaged one-dependence estimators (AODE) is a probabilistic classification learning technique. It was developed to address the attribute-independence problem of the popular naive Bayes classifier. It frequently develops substantially more accurate classifiers than naive Bayes at the cost of a modest increase in the amount of computation.[1]
Contributeurs: Claire Gorjux, Imane Meziani, wiki
