Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| Regresioni Logjistik i Grupit× | Regresioni logjistik gjysmë-mbikëqyrës× | |
|---|---|---|
| Fusha | Mësimi i makinës | Mësimi i makinës |
| Familja | Machine learning | Machine learning |
| Viti i origjinës≠ | 1996–2000s | 1995–2000 |
| Krijuesi≠ | Breiman, L. (bagging); broader ensemble literature | Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training) |
| Lloji≠ | Ensemble of logistic regression classifiers | Semi-supervised classifier |
| Burimi themelues≠ | Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. DOI ↗ | Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134. DOI ↗ |
| Emërtime të tjera | logistic regression ensemble, bagged logistic regression, aggregated logistic regression, logistic ensemble classifier | SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier |
| Të lidhura≠ | 6 | 5 |
| Përmbledhja≠ | Ensemble Logistic Regression trains multiple logistic regression classifiers on varied subsets or perturbations of the training data and combines their probability estimates by averaging or voting. The approach preserves logistic regression's probabilistic interpretability while reducing variance and improving predictive stability through aggregation. | 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. |
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