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| ブースティング× | ナイーブベイズ× | |
|---|---|---|
| 分野 | 機械学習 | 機械学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 1990–1997 | 1997 |
| 提唱者≠ | Schapire, R. E.; Freund, Y. | Mitchell, T. M. (textbook treatment) |
| 種類≠ | Sequential ensemble (iterative reweighting) | Probabilistic classifier (Bayes' theorem with conditional independence) |
| 原典≠ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| 別名≠ | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| 関連≠ | 6 | 4 |
| 概要≠ | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. | Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate. |
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