Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Логистическая регрессия с частичной разметкой× | Обучение с частичной разметкой× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1995–2000 | 1970s–2006 (formalized) |
| Автор метода≠ | Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training) | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Тип≠ | Semi-supervised classifier | Learning paradigm |
| Основополагающий источник≠ | Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134. DOI ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Другие названия | SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Связанные | 5 | 5 |
| Сводка≠ | 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 learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
| ScholarGateНабор данных ↗ |
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