قارن الطرق

	الانحدار الخطي شبه المُشرف عليه	الانحدار الخطي (تعلم الآلة)
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2005–2006	1805–1809
صاحب الطريقة	Chapelle, O.; Scholkopf, B.; Zien, A. (seminal synthesis); Zhou & Li (co-training formulation)	Legendre, A.-M. & Gauss, C.F.
النوع	Semi-supervised regression model	Supervised regression
المصدر التأسيسي	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 3). Springer. ISBN: 978-0-387-84858-7
الأسماء البديلة	SSL linear regression, semi-supervised least squares, transductive linear regression, label-efficient linear regression	ordinary least squares regression, OLS, least squares regression, multiple linear regression
ذات صلة	4	5
الملخص	Semi-supervised linear regression fits a linear model on a small labeled dataset and then leverages a larger pool of unlabeled observations to improve coefficient estimates and generalization. By generating pseudo-labels for unlabeled points and iteratively refining the model, it achieves better predictive accuracy than a purely supervised linear model trained on scarce labels alone.	Linear regression fits a straight-line relationship between one or more input features and a continuous numeric outcome by minimising the sum of squared prediction errors. As a machine-learning model it is trained on labeled examples and evaluated on held-out data, making it the simplest supervised learning baseline for any regression task.