Semi-Supervised Active Learning Algorithm: Difference between revisions
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** It can be applied by a [[Semi-Supervised Active Learning System]] (to solve a [[Semi-Supervised Active Learning Task]]). | ** It can be applied by a [[Semi-Supervised Active Learning System]] (to solve a [[Semi-Supervised Active Learning Task]]). | ||
* <B>Example(s):</B> | * <B>Example(s):</B> | ||
** | ** that described in [[2009_SemiSupervisedActiveLearningfor|Tomanek & Hahn (2009)]]. | ||
** … | ** … | ||
* <B>Counter-Example(s):</B> | * <B>Counter-Example(s):</B> | ||
Revision as of 23:30, 8 May 2022
A Semi-Supervised Active Learning Algorithm is an active learning algorithm that is a semi-supervised learning algorithm.
- Context:
- It can be applied by a Semi-Supervised Active Learning System (to solve a Semi-Supervised Active Learning Task).
- Example(s):
- that described in Tomanek & Hahn (2009).
- …
- Counter-Example(s):
- See: Unsupervised Learning.
References
2015
- (Dai & Le, 2015) ⇒ Andrew M. Dai, and Quoc V. Le. (2015). “Semi-supervised Sequence Learning.” In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2.
2009
- (Tomanek & Hahn, 2009) ⇒ Katrin Tomanek, and Udo Hahn. (2009). “Semi-supervised Active Learning for Sequence Labeling.” In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP (ACL 2009).