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| Naive Bayes auto-supervisionato× | Naive Bayes Semi-Supervisionato× | |
|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine | 2000 | 2000 |
| Ideatore | Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T. | Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T. |
| Tipo≠ | Self-supervised generative classifier | Semi-supervised generative classifier |
| Fonte seminale≠ | 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 ↗ | 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 ↗ |
| Alias | Self-training Naive Bayes, EM Naive Bayes, Expectation-Maximization Naive Bayes, Pseudo-label Naive Bayes | SSL Naive Bayes, EM-Naive Bayes, semi-supervised generative classifier, Nigam et al. text classifier |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | Self-supervised Naive Bayes extends the classic Naive Bayes classifier to exploit large pools of unlabeled data by iteratively assigning soft pseudo-labels through an Expectation-Maximization loop. Originally demonstrated for text classification by Nigam et al. (2000), the approach can substantially improve accuracy when labeled examples are scarce but unlabeled data are plentiful. | 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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