[PDF] Top 20 Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability
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Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability
... automatic model con- struction algorithm that directly optimizes model general- ization ...an orthogonal forward regression framework, which minimizes the effort in the ... See full document
6
Sparse modelling using orthogonal forward regression with PRESS statistic and regularization
... the model simply fits the training data well. Rather, the goodness of a model is characterized by its generalization capability, inter- pretability and ease for knowledge ...possible ... See full document
14
Probability density function estimation using orthogonal forward regression
... simple regression- based alternative, which directly uses the PW estimate as the desired ...algorithm based on the LOO MSE and local regularisation [12] can readily be employed to select a ... See full document
6
Model structure selection in powertrain calibration and control
... developed based on PFI gasoline engine in Chapter 4. The model structures were found by Matlab identification toolbox and validated by the measured ...al model structure was adopted, especially, the ... See full document
158
M estimator and D optimality model construction using orthogonal forward regression
... used. Based on the modified Gram–Schmidt algorithm, a few variants of forward OLS algorithms have been introduced to improve model generalization capability based on the concepts ... See full document
8
Automatic kernel regression modelling using combined leave one out test score and regularised orthogonal least squares
... robust model structure selection are effective and complementary approaches for robust modelling, it is highly desirable to develop algorithms by combining parameter regularisation with model structure ... See full document
18
Sparse kernel density construction using orthogonal forward regression with leave one out test score and local regularization
... estimates based on a regression approach that directly optimizes model generalization ...an orthogonal forward regression, and the algorithm incrementally minimizes ... See full document
10
Sparse kernel density construction using orthogonal forward regression with leave one out test score and local regularization
... estimates based on a regression approach that directly optimizes model generalization ...an orthogonal forward regression, and the algorithm incrementally minimizes ... See full document
10
A forward regression algorithm based on M estimators
... The orthogonal forward regression (OFR) is an efficient algorithm to determine a parsimonious model structure ...improved model generaliza- tion, a few variants of OFR have been ... See full document
5
Sparse support vector regression based on orthogonal forward selection for the generalised kernel model
... An orthogonal forward selection procedure has been proposed to construct a sparse generalised kernel model ...each model construction stage, a kernel regressor is optimised using a global ... See full document
14
Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model
... system model will have an improved performance. A limitation of the SVM-based regression modelling techniques is the fact that the kernel centres or mean vectors are typically placed at the training ... See full document
12
Kernel density construction using orthogonal forward regression
... estimates based on a regression approach that directly optimizes generalization ...an orthogonal forward regression, and the algorithm incrementally minimizes the ... See full document
6
An extended orthogonal forward regression algorithm for system identification using entropy
... A central part of the conventional OFR algorithm is the Error Reduction Ratio (ERR). The ERR of a term represents the percentage reduction in the total mean square error by including this specific term in the final ... See full document
23
An Orthogonal Forward Regression Algorithm Combined with Basis Pursuit and D Optimality
... new forward regression model identification algorithm is ...derived model parameters, in each forward regression step, are initially estimated via orthogonal least squares ... See full document
6
Local Regularization Assisted Orthogonal Least Squares Regression
... possible model that explains the data. The orthogonal least squares (OLS) algorithm [9,11] is an efficient learning procedure for constructing sparse regression ...selected model regressor to ... See full document
27
Maximizing the capability of wireless sensor networks
... 1 1 Figure 3.2 Data retrieved by using the Continuous ON scheme and the ON/OFF scheme different numbers of with ON/OFF periods; also shown is the maximum delay associated with the 15 ON/[r] ... See full document
82
Identification of nonlinear systems with non-persistent excitation using an iterative forward orthogonal least squares regression algorithm
... iterative orthogonal least squares forward regression (iOFR) algorithm is proposed to identify nonlinear systems which may not be persistently ...classic forward orthogonal ... See full document
14
Modelling the Nonlinear Oscillations Due to Vertical Bouncing Using a Multi-Scale Restoring Force System Identification Method
... A very important issue encountered when applying the restoring force surface method is to design an appropriate excitation signals. One of the important criteria is that the phase trajectory covers as much of the phase ... See full document
23
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] ... See full document
21
Optimum binary cut-off threshold of a diagnostic test: comparison of different methods using Monte Carlo technique
... logistic regression ana- lyses, the sigmoidal post-test probability curves are steeper with respect to variation of test value x when we employ the estimates of logistic regression analyses obtained at ... See full document
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