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| 앙상블 나이브 베이즈× | 준지도 학습 나이브 베이즈× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2000s | 2000 |
| 창시자≠ | Various (Dietterich, T.G.; Webb, G.I.; others) | Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T. |
| 유형≠ | Ensemble of probabilistic classifiers | Semi-supervised generative classifier |
| 원전≠ | Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. In J. Kittler & F. Roli (Eds.), Multiple Classifier Systems (MCS 2000), Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer. DOI ↗ | Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2–3), 103–134. DOI ↗ |
| 별칭 | Bagged Naive Bayes, Boosted Naive Bayes, Naive Bayes ensemble, NB ensemble | SSL Naive Bayes, EM-Naive Bayes, semi-supervised generative classifier, Nigam et al. text classifier |
| 관련≠ | 6 | 4 |
| 요약≠ | Ensemble Naive Bayes trains multiple Naive Bayes classifiers — each exposed to a different view of the data through bagging, feature subsets, or boosting — and combines their probabilistic predictions by voting or probability averaging. The approach retains the speed and interpretability of individual Naive Bayes models while reducing variance and improving accuracy through ensemble aggregation. | Semi-supervised Naive Bayes extends the classic Naive Bayes generative model to exploit large pools of unlabeled data alongside a small labeled set. Using Expectation-Maximization, it iteratively infers soft class assignments for unlabeled examples and re-estimates class and feature parameters, yielding substantially better classifiers when labeled examples are scarce. |
| ScholarGate데이터셋 ↗ |
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