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| Curriculum-Lernen× | Aktives Lernen× | |
|---|---|---|
| Fachgebiet≠ | Deep Learning | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr | 2009 | 2009 |
| Urheber≠ | Yoshua Bengio et al. | Burr Settles |
| Typ≠ | Training strategy | Interactive supervised learning framework |
| Wegweisende Quelle≠ | Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. International Conference on Machine Learning (ICML), 41–48. DOI ↗ | Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648. link ↗ |
| Aliasnamen | Scheduled Training, Difficulty-Based Training, Self-Paced Learning, Müfredat Öğrenimi | Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme |
| Verwandt≠ | 3 | 2 |
| Zusammenfassung≠ | Curriculum Learning is a training strategy for machine learning models, introduced by Bengio et al. in 2009, in which training examples are presented in a meaningful order—typically from easy to hard—rather than at random. Inspired by how humans and animals learn progressively, it organizes training data into a curriculum that starts with simpler, cleaner, or more representative samples and gradually introduces harder or more complex examples as the model matures. | 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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