Linganisha mbinu

	Support Vector Machine yenye Regulareshini	Uchambuzi wa Ubaguzi wa Laini (LDA)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Latent structure
Mwaka wa asili	1995–2004	1936
Mwanzilishi	Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)	Fisher, R. A.
Aina	Regularized discriminative classifier / regressor	Supervised dimensionality reduction and linear classifier
Chanzo asilia	Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.
Majina mbadala	Regularized SVM, L1-SVM, L2-SVM, penalized SVM	LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis
Zinazohusiana	4	4
Muhtasari	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.	Linear Discriminant Analysis is a supervised method for dimensionality reduction and classification, introduced by Ronald A. Fisher in 1936, that finds linear combinations of features which maximally separate predefined classes while preserving as much class-discriminatory information as possible. It simultaneously serves as a feature-projection technique and a probabilistic classifier, making it one of the foundational methods in pattern recognition and statistical learning.