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

The method of least squares is a parameter estimation method in regression analysis based on minimizing the sum of the squares of the residuals (a residual...

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

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

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

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

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...

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:...

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

kernel function. 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...