Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Онлайн-обучение с переносом знаний (Online Transfer Learning, OTL)× | Обучение с частичной разметкой× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2010 | 1970s–2006 (formalized) |
| Автор метода≠ | Zhao, P. & Hoi, S. C. H. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Тип≠ | Online learning with source-domain knowledge transfer | Learning paradigm |
| Основополагающий источник≠ | Zhao, P., & Hoi, S. C. H. (2010). OTL: A Framework of Online Transfer Learning. In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 1231–1238. Omnipress. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Другие названия | OTL, streaming transfer learning, incremental transfer learning, online domain adaptation | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Связанные≠ | 4 | 5 |
| Сводка≠ | Online Transfer Learning (OTL) extends transfer learning to sequential, streaming settings: instead of training on a fixed dataset, the model processes examples one at a time and simultaneously leverages knowledge from a related source domain to improve predictions on the target domain without requiring large labeled target datasets upfront. | 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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