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| Робусно активно учење× | [CYRILLIC SCRIPT DETECTED - NEEDS LATIN CONVERSION]× | |
|---|---|---|
| Oblast | Mašinsko učenje | Mašinsko učenje |
| Porodica | Machine learning | Machine learning |
| Godina nastanka≠ | 2006 | 1958–2000s |
| Tvorac≠ | Balcan, M.-F.; Beygelzimer, A.; Langford, J. | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| Tip≠ | Active learning with robustness guarantees | Learning paradigm (sequential model update) |
| Temeljni izvor≠ | 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 ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| Drugi nazivi | RAL, noise-tolerant active learning, robust query learning, adversarially robust active learning | incremental learning, sequential learning, streaming learning, online machine learning |
| Srodne | 6 | 6 |
| Sažetak≠ | 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. | Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight. |
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