Σύγκριση μεθόδων

	Παλινδρόμηση Υποστηρικτικών Διανυσμάτων	Παλινδρόμηση Lasso
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2004	1996
Δημιουργός	Smola, A.J. & Schölkopf, B.	Tibshirani, R.
Τύπος	Kernel-based supervised model (epsilon-insensitive regression)	Regularized linear regression (L1 penalty)
Θεμελιώδης πηγή	Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Εναλλακτικές ονομασίες	Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Συναφείς	4	4
Σύνοψη	Support Vector Regression (SVR), described in Smola and Schölkopf's 2004 tutorial, predicts a continuous outcome by fitting a function that stays within an epsilon-wide tube around the data while incurring as little error as possible. It extends the support vector machine idea from classification to regression, using a kernel to capture nonlinear relationships.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.