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Generalized and Robust Least Squares Regression

IEEE Transactions on Neural Networks and Learning Systems
As a simple yet effective method, least squares regression (LSR) is extensively applied for data regression and classification. Combined with sparse representation, LSR can be extended to feature selection (FS) as well, in which l1 regularization is often applied in embedded FS algorithms.
Jingyu Wang   +3 more
openaire   +4 more sources

Generalized Least Squares

2004
Preface.1 Preliminaries.1.1 Overview.1.2 Multivariate Normal and Wishart Distributions.1.3 Elliptically Symmetric Distributions.1.4 Group Invariance.1.5 Problems.2 Generalized Least Squares Estimators.2.1 Overview.2.2 General Linear Regression Model.2.3 Generalized Least Squares Estimators.2.4 Finiteness of Moments and Typical GLSEs.2.5 Empirical ...
Takeaki Kariya, Hiroshi Kurata
openaire   +2 more sources

General fuzzy least squares

Fuzzy Sets and Systems, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ming, Ma   +2 more
openaire   +2 more sources

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