About: Averaged one-dependence estimators

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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.

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  • 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. (en)
  • AODE（Averaged One-Dependence Estimators）は確率的分類器の一つである。AODEは、代表的な確率的分類器である単純ベイズ分類器の単純な条件付き独立の仮定を緩和する分類器として考案された。多くの場合、単純ベイズ分類器に対して顕著に高い精度を示すが、計算コストもそれほど大きくはならないことが示された。 (ja)
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  • 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. (en)
  • AODE（Averaged One-Dependence Estimators）は確率的分類器の一つである。AODEは、代表的な確率的分類器である単純ベイズ分類器の単純な条件付き独立の仮定を緩和する分類器として考案された。多くの場合、単純ベイズ分類器に対して顕著に高い精度を示すが、計算コストもそれほど大きくはならないことが示された。 (ja)
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  • Averaged one-dependence estimators (en)
  • AODE (ja)
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