Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастное активное обучение× | Обучение с частичной разметкой× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2006 | 1970s–2006 (formalized) |
| Автор метода≠ | Balcan, M.-F.; Beygelzimer, A.; Langford, J. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Тип≠ | Active learning with robustness guarantees | Learning paradigm |
| Основополагающий источник≠ | Balcan, M.-F., Beygelzimer, A., & Langford, J. (2006). Agnostic active learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), pp. 65–72. ACM. DOI ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Другие названия | RAL, noise-tolerant active learning, robust query learning, adversarially robust active learning | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Robust Active Learning extends the standard active learning framework to handle noisy labels, adversarial perturbations, and unreliable oracles. Rather than assuming perfect labeling, it incorporates statistical or adversarial robustness guarantees into the query selection process, maintaining sample efficiency while tolerating corruption in the annotation process. | 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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