Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting...

In regression analysis, least squares is a parameter estimation method in which the sum of the squares of the residuals (a residual being the difference...

intersection Line fitting Nonlinear least squares Regularized least squares Simple linear regression Partial least squares regression Linear function Weisstein...

}}} and is therefore equivalent to Bayesian linear regression. Regularized least squares: the elements of β {\displaystyle {\boldsymbol {\beta }}} must...

interpretation of regularization Bias–variance tradeoff Matrix regularization Regularization by spectral filtering Regularized least squares Lagrange multiplier...

corollary of (2) In a vector and kernel notation, the problem of regularized least squares can be rewritten as: min c ∈ R n 1 n ‖ Y − K c ‖ R n 2 + λ ⟨ c...

the families of support vector machines and regularized least squares algorithms. (Regularized least squares includes the ridge regression algorithm; the...

In statistics, generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model. It is used when there...

Weighted least squares (WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge...

standard regression will fail in these cases (unless it is regularized). Partial least squares was introduced by the Swedish statistician Herman O. A. Wold...

Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters...

set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable...

algorithms: regularized least squares and support vector machines (SVM) to semi-supervised versions Laplacian regularized least squares and Laplacian...

reduces to ordinary least squares. A more general approach to Tikhonov regularization is discussed below. Tikhonov regularization was invented independently...

as regularized least-squares and logistic regression. The difference between the three lies in the choice of loss function: regularized least-squares amounts...

In applied statistics, total least squares is a type of errors-in-variables regression, a least squares data modeling technique in which observational...

Least-squares adjustment is a model for the solution of an overdetermined system of equations based on the principle of least squares of observation residuals...

The method of iteratively reweighted least squares (IRLS) is used to solve certain optimization problems with objective functions of the form of a p-norm:...

gives rise to several well-known learning algorithms such as regularized least squares and support vector machines. A purely online model in this category...

School, California Recursive least squares filter, in minimisation Regularized least squares, for solving least-squares problems Reef Life Survey, a marine...

damped least-squares (DLS) method, is used to solve non-linear least squares problems. These minimization problems arise especially in least squares curve...

Constrained least squares Regularized least squares Tikhonov regularization Spike and slab variable selection Bayesian interpretation of kernel regularization Huang...

Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM)...

version of the least squares cost function as in ridge regression (L2-norm penalty) and lasso (L1-norm penalty). Use of the Mean Squared Error (MSE) as...

ISSN 1533-7928. Rifkin, Ryan M.; Lippert, Ross A. (1 May 2007), Notes on Regularized Least Squares (PDF), MIT Computer Science and Artificial Intelligence Laboratory...

mathematical optimization, the problem of non-negative least squares (NNLS) is a type of constrained least squares problem where the coefficients are not allowed...

are not based on ordinary least squares projections, but rather on regularized (generalized and/or penalized) least-squares, and so degrees of freedom...

statistics and machine learning, lasso (least absolute shrinkage and selection operator; also Lasso, LASSO or L1 regularization) is a regression analysis method...

Polynomial regression models are usually fit using the method of least squares. The least-squares method minimizes the variance of the unbiased estimators of...

y} as much as possible. Regularized least squares Bayesian linear regression Bayesian interpretation of Tikhonov regularization Álvarez, Mauricio A.; Rosasco...