পদ্ধতির তুলনা করুন

	নিয়ন্ত্রিত সাপোর্ট ভেক্টর মেশিন	নিয়মিত লজিস্টিক রিগ্রেশন
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1995–2004	1996–2005
প্রবর্তক	Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)
ধরন	Regularized discriminative classifier / regressor	Penalized classification model
মৌলিক উৎস	Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
অপর নাম	Regularized SVM, L1-SVM, L2-SVM, penalized SVM	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression
সম্পর্কিত	4	5
সারসংক্ষেপ	Regularized Support Vector Machine extends the classic SVM by explicitly controlling the trade-off between margin maximization and training error through an L1 or L2 penalty parameter. The soft-margin formulation introduced by Cortes and Vapnik in 1995 is itself a regularized model, and later L1-SVM variants additionally promote feature sparsity, enabling automatic variable selection in high-dimensional settings.	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.