Сравнение методов

	Регуляризованное полусупервизорное обучение	Регуляризованная логистическая регрессия
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2006	1996–2005
Автор метода	Belkin, M.; Niyogi, P.; Sindhwani, V.	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)
Тип	Regularized learning paradigm	Penalized classification model
Основополагающий источник	Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Другие названия	manifold regularization, graph-regularized SSL, semi-supervised regularization, Laplacian regularization	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression
Связанные	6	5
Сводка	Regularized semi-supervised learning adds explicit geometric or graph-based penalty terms to a semi-supervised objective so that the decision function varies smoothly over the data manifold. Pioneered through manifold regularization (Belkin, Niyogi & Sindhwani, 2006), it exploits the structure of both labeled and unlabeled examples to learn more accurate models than supervised regularization alone when labeled data are scarce.	Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces.