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| オンライン半教師あり学習× | 半教師あり学習× | |
|---|---|---|
| 分野 | 機械学習 | 機械学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2000s–2010s | 1970s–2006 (formalized) |
| 提唱者≠ | Goldberg, A., Li, M., & Zhu, X. (and others in stream learning community) | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| 種類≠ | Incremental / stream-based semi-supervised learning framework | Learning paradigm |
| 原典≠ | Goldberg, A., Li, M., & Zhu, X. (2008). Online manifold regularization: A new learning setting and empirical study. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 393–407. Springer. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| 別名 | stream-based semi-supervised learning, incremental semi-supervised learning, online SSL, semi-supervised online learning | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| 関連≠ | 6 | 5 |
| 概要≠ | Online semi-supervised learning combines the incremental, one-pass nature of online learning with the ability to exploit unlabeled data alongside sparse labeled observations. It is designed for settings where data arrives as a stream and obtaining labels for every instance is expensive or impractical — such as real-time classification of web content, sensor readings, or social media posts. | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
| ScholarGateデータセット ↗ |
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