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| 준지도 학습 로지스틱 회귀× | 준지도 학습 나이브 베이즈× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1995–2000 | 2000 |
| 창시자≠ | Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training) | Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T. |
| 유형≠ | Semi-supervised classifier | Semi-supervised generative classifier |
| 원전≠ | Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134. 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 | SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier | SSL Naive Bayes, EM-Naive Bayes, semi-supervised generative classifier, Nigam et al. text classifier |
| 관련≠ | 5 | 4 |
| 요약≠ | Semi-supervised logistic regression extends the standard logistic classifier by incorporating unlabeled data during training. Using self-training, expectation-maximization, or label-propagation wrappers, it iteratively assigns soft labels to unlabeled examples and refines model parameters, improving generalization when labeled data are scarce relative to the full dataset. | 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. |
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