This paper tries to compare more accurate and efficient L1 norm regression algorithms. Other comparative studies are mentioned, and their conclusions are discussed. Many experiments have been performed to evaluate the comparative efficiency and accuracy of the selected algorithms
When estimating the parameters in a linear regression model, the method of least squares (L^-norm es...
Some of the result included in this paper were presented at the 6 World Congress of the Econometric ...
In the maintenance phase, the regression test selection problem refers to selecting test cases from ...
In this paper, we propose four algorithms for L1 norm computation of regression parameters, where tw...
In this paper, three algorithms for weighted median, simple linear, and multiple m parameters L1 nor...
This paper gives a rather general review of the L1 norm algorithms. The chronology and historical de...
We present a survey of possible algorithms and their rounding off trancation, arithmetic error bound...
This thesis is focused on the L1 regression, a possible alternative to the ordinary least squares re...
Abstract: We have comparatively assessed five regression performance metrics namely, Mean Absolute E...
In recent years, neural networks are widely being used in areas where conventional statistical metho...
In the specialized literature, researchers can find a large number of proposals for solving regressi...
This paper discusses estimation of regression model with LASSO penalty when the L1-norm is replaced ...
This paper is a survey on traditional linear regression techniques using the lñ-, l2-, and lâÂÂ-n...
This research uses four classification algorithms in standard and boosted forms to predict members o...
Available from British Library Document Supply Centre- DSC:DX173487 / BLDSC - British Library Docume...
When estimating the parameters in a linear regression model, the method of least squares (L^-norm es...
Some of the result included in this paper were presented at the 6 World Congress of the Econometric ...
In the maintenance phase, the regression test selection problem refers to selecting test cases from ...
In this paper, we propose four algorithms for L1 norm computation of regression parameters, where tw...
In this paper, three algorithms for weighted median, simple linear, and multiple m parameters L1 nor...
This paper gives a rather general review of the L1 norm algorithms. The chronology and historical de...
We present a survey of possible algorithms and their rounding off trancation, arithmetic error bound...
This thesis is focused on the L1 regression, a possible alternative to the ordinary least squares re...
Abstract: We have comparatively assessed five regression performance metrics namely, Mean Absolute E...
In recent years, neural networks are widely being used in areas where conventional statistical metho...
In the specialized literature, researchers can find a large number of proposals for solving regressi...
This paper discusses estimation of regression model with LASSO penalty when the L1-norm is replaced ...
This paper is a survey on traditional linear regression techniques using the lñ-, l2-, and lâÂÂ-n...
This research uses four classification algorithms in standard and boosted forms to predict members o...
Available from British Library Document Supply Centre- DSC:DX173487 / BLDSC - British Library Docume...
When estimating the parameters in a linear regression model, the method of least squares (L^-norm es...
Some of the result included in this paper were presented at the 6 World Congress of the Econometric ...
In the maintenance phase, the regression test selection problem refers to selecting test cases from ...
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