Robust Active Learning
Robust Active Learning udvider det standardmæssige active learning-framework til at håndtere støjende labels, adversariale perturbationer og upålidelige elleracles. I stedet for at antage perfekt annotering, inkorporerer det statistiske eller adversariale robusthedsgarantier i udvælgelsesprocessen for forespørgsler, hvilket bevarer sampleffektivitet, mens det tolererer korruption i annoteringsprocessen.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- 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: 10.1145/1143844.1143853 ↗
- Settles, B. (2009). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison. link ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Robust Active Learning (Noise-Tolerant Query-Based Learning). ScholarGate. https://scholargate.app/da/machine-learning/robust-active-learning
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Aktiv læringMaskinlæring↔ compare
- Few-shot LearningMaskinlæring↔ compare
- Online læringMaskinlæring↔ compare
- Robust Random ForestMaskinlæring↔ compare
- Robust Support Vector-maskineMaskinlæring↔ compare
- Semi-supervised LearningMaskinlæring↔ compare
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