Add to ORKGThe paper proposes a locally regularised orthogonal least squares (LROLS) algorithm for constructing sparse multi-output regression models that generalise well. By associating each regressor in the regression model with an individual regularisation parameter, the ability for the multi-output orthogonal least squares (OLS) model selection to produce a parsimonious model with good generalisation performance is greatly enhanced
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
This paper introduces an automatic robust nonlinear identification algorithm using the leave-one-out...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
ARTICLE IN PRESS www.elsevier.com/locate/neucom A locally regularized orthogonal least squares (LROL...
The paper proposes to combine an orthogonal least squares (OLS) model selection with local regularis...
A locally regularized orthogonal least squares (LROLS) algorithm is proposed for constructing parsim...
Combining orthogonal least squares (OLS) model selection with local regularisation or smoothing lead...
The note proposes an efficient nonlinear identification algorithm by combining a locally regularized...
The paper proposes to combine a locally regularized orthogonal least squares (LROLS) model selection...
The paper proposes to combine a locally regularized orthogonal least squares (LROLS) model selection...
The paper proposes an efficient nonlinear identification algorithm by combining a locally regularize...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
A unified approach is proposed for data modelling that includes supervised regression and classifica...
International audienceWe propose and analyse a reduced-rank method for solving least-squares regress...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
This paper introduces an automatic robust nonlinear identification algorithm using the leave-one-out...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
ARTICLE IN PRESS www.elsevier.com/locate/neucom A locally regularized orthogonal least squares (LROL...
The paper proposes to combine an orthogonal least squares (OLS) model selection with local regularis...
A locally regularized orthogonal least squares (LROLS) algorithm is proposed for constructing parsim...
Combining orthogonal least squares (OLS) model selection with local regularisation or smoothing lead...
The note proposes an efficient nonlinear identification algorithm by combining a locally regularized...
The paper proposes to combine a locally regularized orthogonal least squares (LROLS) model selection...
The paper proposes to combine a locally regularized orthogonal least squares (LROLS) model selection...
The paper proposes an efficient nonlinear identification algorithm by combining a locally regularize...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
A unified approach is proposed for data modelling that includes supervised regression and classifica...
International audienceWe propose and analyse a reduced-rank method for solving least-squares regress...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
This paper introduces an automatic robust nonlinear identification algorithm using the leave-one-out...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...