So sánh phương pháp

	Hồi quy Logistic bán giám sát	Học bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1995–2000	1970s–2006 (formalized)
Người khởi xướng	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Loại	Semi-supervised classifier	Learning paradigm
Công trình gốc	Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Tên gọi khác	SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Liên quan	5	5
Tóm tắt	Semi-supervised logistic regression extends the standard logistic classifier by incorporating unlabeled data during training. Using self-training, expectation-maximization, or label-propagation wrappers, it iteratively assigns soft labels to unlabeled examples and refines model parameters, improving generalization when labeled data are scarce relative to the full dataset.	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.