Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Активное обучение с K-ближайшими соседями (K-Nearest Neighbors, KNN)× | Полусупервизорный метод K-ближайших соседей× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1951–2010 | 2002 (semi-supervised extension); 1967 (KNN base) |
| Автор метода≠ | Settles, B. (active learning framework); Fix & Hodges (KNN base) | Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base) |
| Тип≠ | Active learning with KNN base learner | Semi-supervised classifier / label propagation |
| Основополагающий источник≠ | Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison. link ↗ | Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| Другие названия | AL-KNN, active KNN, query-based nearest neighbor learning, uncertainty-sampling KNN | SS-KNN, semi-supervised KNN, KNN label propagation, graph-based semi-supervised KNN |
| Связанные | 4 | 4 |
| Сводка≠ | Active learning with K-nearest neighbors combines the instance-based prediction of KNN with an iterative query strategy that selects the most informative unlabeled examples for annotation. The model requests labels only for instances where neighborhood vote margins are narrowest, achieving competitive accuracy with far fewer labeled examples than fully supervised KNN on tabular data. | Semi-supervised KNN extends the classic K-nearest neighbors algorithm to exploit large pools of unlabeled data alongside a small labeled set. By building a KNN graph over all observations and propagating known labels through the graph's edges, the method infers labels for unlabeled points without requiring expensive manual annotation of every sample. |
| ScholarGateНабор данных ↗ |
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