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| Aktives Lernen mit Gauß-Prozessen× | Aktives Lernen× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1992 | 2009 |
| Urheber≠ | MacKay, D. J. C. | Burr Settles |
| Typ≠ | Bayesian active learning | Interactive supervised learning framework |
| Wegweisende Quelle≠ | MacKay, D. J. C. (1992). Information-based objective functions for active data selection. Neural Computation, 4(4), 590–604. DOI ↗ | Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648. link ↗ |
| Aliasnamen | GP active learning, Gaussian process active learning, GP-AL, Bayesian active learning with GP | Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme |
| Verwandt≠ | 4 | 2 |
| Zusammenfassung≠ | Active Learning Gaussian Process (GP-AL) combines a Gaussian process probabilistic model with an active learning query strategy, using the GP's posterior uncertainty to select the most informative unlabeled examples for labeling. This iterative approach minimizes labeling effort while maximizing predictive accuracy, making it ideal when labeled data is scarce or expensive to obtain. | 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. |
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