Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Активное обучение× | Обучение с частичной разметкой× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2009 | 1970s–2006 (formalized) |
| Автор метода≠ | Burr Settles | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Тип≠ | Interactive supervised learning framework | Learning paradigm |
| Основополагающий источник≠ | Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Другие названия | Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Связанные≠ | 2 | 5 |
| Сводка≠ | Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires. | 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 ↗ |
|
|