방법 비교

	정규화된 전이 학습	정규화 로지스틱 회귀
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2000s–2010s	1996–2005
창시자	Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)
유형	Regularized supervised/semi-supervised learning framework	Penalized classification model
원전	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
별칭	regularized domain adaptation, transfer learning with regularization, penalized transfer learning, regularized fine-tuning	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression
관련	6	5
요약	Regularized Transfer Learning applies explicit penalty terms to a transfer learning pipeline to control how much a model shifts away from source-domain knowledge when adapting to a new target domain. The regularizer discourages negative transfer — the harmful carry-over of irrelevant source patterns — while preserving beneficial shared representations and preventing overfitting when target-domain labels 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.