Forward regression
Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems
... Orthogonal Forward Regression) algorithm has therefore been proposed to reduce these problems while maintaining the simplicity of the identification ...squares regression problem by solving a ...
25
Probability density function estimation using orthogonal forward regression
... a regression problem and the orthogonal forward regression tech- nique is adopted to construct sparse kernel density ...this regression- based approach to effectively construct a sparse kernel ...
6
An Orthogonal Forward Regression Algorithm Combined with Basis Pursuit and D Optimality
... new forward regression model identification algorithm is ...each forward regression step, are initially estimated via orthogonal least squares (OLS) (using the modified Gram-Schmidt proce- ...
6
Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability
... ther a separate criterion to terminate the selection procedure nor any iteration of the procedure is needed. The proposed algorithm based on the standard Gram-Schmidt procedure is summarized in Appendix, in which the ...
6
Sparse modelling using orthogonal forward regression with PRESS statistic and regularization
... The evaluation of model generalization capability is directly based on the concept of cross validation [20]. This paper inves- tigates a model construction algorithm using a model selection criterion that is based ...
14
M estimator and D optimality model construction using orthogonal forward regression
... orthogonal forward regression (OFR) model identification algorithm using D-optimality for model structure selection and is based on an M-estimators of parameter ...orthogonal forward ...
8
An iterative orthogonal forward regression algorithm
... traditional forward regression methods, the OFR algorithm is computationally efficient because it successfully avoids the re-estimation of the parameters and evaluates the contribution of each term ...the ...
28
Sparse model identification using orthogonal forward regression with basis pursuit and D optimality
... the forward orthogonal least- square algorithm using the modified Gram – Schmidt ...each forward regression step, with the basis pursuit that minimises the l 1 norm of the parameter estimates ...with ...
8
Kernel density construction using orthogonal forward regression
... a regression approach that directly optimizes generalization ...orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test ...
6
Sparse kernel density construction using orthogonal forward regression with leave one out test score and local regularization
... orthogonal forward regression (OFR) that incrementally minimizes the training mean square error (MSE) ...the regression model construction algorithm [16] to the construction of sparse kernel density ...
10
A forward regression algorithm based on M estimators
... This paper introduces an orthogonal forward regression (OFR) model structure selection algorithm based on the M- estimators. The basic idea of the proposed approach is to incorporate an IRLS inner loop into ...
5
A modified orthogonal forward regression least-squares algorithm for system modelling from noisy regressors
... Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the rea[r] ...
21
An extended orthogonal forward regression algorithm for system identification using entropy
... Orthogonal Forward Re- gression (OFR) algorithm so that instead of using the Error Reduction Ratio (ERR) for term selection, a new optimality criterion —Shannon’s Entropy Power Reduction Ratio (EPRR) is introduced ...
23
Probability density function estimation using orthogonal forward regression
... Outline o Motivations/overview for sparse kernel density estimation o Proposed sparse kernel density estimator: m Convert unsupervised density learning into constrained regression by ado[r] ...
18
Automatic kernel regression modelling using combined leave one out test score and regularised orthogonal least squares
... ridge regression and model optimal generalisation structure ...squares forward regression framework and hence construct a model with a good generalisation ...
18
asy064.pdf
... the forward regression approach (7), the semiparametric inverse regression approach (14), the counting process inverse regres- sion approach (16), and the computationally efficient approach ...inverse ...
16
Regularized orthogonal least squares algorithm for constructing radial basis function networks
... The proposed algorithm combines the advantages of both the orthogonal forward regression and regularization methods to provide an efficient and powerful procedure for constructing parsim[r] ...
10
A resolution of the forward discount puzzle
... the forward bias is the result of either rational or irrational ...conventional regression techniques, Baillie and Bollerslev (2000) argue that there may be biases in the conventional regression ...
34
Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model
... SVM-based regression modelling techniques is the fact that the kernel centres or mean vectors are typically placed at the training input data and a fixed common kernel variance is used for all the regressor ...
12
Statistical Modelling of CO2 Emissions in Malaysia and Thailand
... residual sum of squares (RSS) multiplied by a penalty factor which would depend on the model complexity. Model with higher complexity generally will decrease the RSS but increase the penalty. These criteria thus allow ...
6