قارن الطرق

	التعلم شبه المُشرف عبر الإنترنت	التعلم شبه المُشرف
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2000s–2010s	1970s–2006 (formalized)
صاحب الطريقة	Goldberg, A., Li, M., & Zhu, X. (and others in stream learning community)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
النوع	Incremental / stream-based semi-supervised learning framework	Learning paradigm
المصدر التأسيسي	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.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
الأسماء البديلة	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
ذات صلة	6	5
الملخص	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.