[PDF] Top 20 A modified orthogonal forward regression least-squares algorithm for system modelling from noisy regressors
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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
Orthogonal least squares regression with tunable kernels
... data modelling is the parsimonious principle of ensuring the smallest possible model that explains the training ...kernel modelling methods [1]–[6] place kernel centres at the training input data and adopt ... See full document
5
Sparse model identification using orthogonal forward regression with basis pursuit and D optimality
... nonlinear modelling using associative memory networks or fuzzy logic has been the problem of the curse of dimensionality ...inference system (ANFIS) [6], Takagi and Sugeno model [7], ...an orthogonal ... See full document
8
A forward regression algorithm based on M estimators
... The modified Gram-Schmidt procedure can be used to perform the orthogonalization and parameter estimation, usually with parameters derived as least squares parame- ...identification algorithm ... See full document
5
Sparse modelling using orthogonal forward regression with PRESS statistic and regularization
... The orthogonal least squares (OLS) algorithm [1], which was developed in the late 1980s for nonlinear system modeling, remains popular for nonlinear data modeling practicians because ... See full document
14
An iterative orthogonal forward regression algorithm
... associated Orthogonal Forward Regression (OFR) algorithm have been widely applied in nonlinear system identification including in the modelling of many engineering, chemical, ... See full document
28
Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model
... mapping from the input space to the high dimensional space is defined implicitly by this kernel ...produced system model will have an improved ...SVM-based regression modelling techniques is ... See full document
12
An Orthogonal Forward Regression Algorithm Combined with Basis Pursuit and D Optimality
... non-linear modelling using associative memory networks or fuzzy logic has been the problem of the curse of dimensionality ...An orthogonal least squares (OLS) algorithm including ... See full document
6
Sparse multi output radial basis function network construction using combined locally regularised orthogonal least square and D Optimality experimental design
... of regressors than the former approach to achieve the same modelling ...LROLS algorithm [22] with the D-optimality experimental ...resulting algorithm is ensured by the orthogonal ... See full document
20
Identification of nonlinear systems with non-persistent excitation using an iterative forward orthogonal least squares regression algorithm
... nonlinear system identification methods, the NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous input) model and the associated Orthogonal Forward Regression (OFR) algorithm ... See full document
14
Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks
... To understand the motivations of using a global optimization method to learn the regularization parameter and width, it is best to examine the generalization performance as a function of these two parameters. It is often ... See full document
5
A multiple sequential orthogonal least squares algorithm for feature ranking and subset selection
... clear from Table II that there is a steep change in the total error reduction ratio, TERR[d], from d=2 to d=3, and from d=3 TERR[d] becomes ... See full document
18
Evaluation of multiple variate selection methods from a biological perspective: a nutrigenomics case study
... sets from Molecular signature database (MSigDB) C2 curated gene sets collection ...identifiers from the gene expression data set) into gene symbols, 9,985 genes were recognized and used for the ... See full document
11
ESTIMATION ACCURACY OF EXPONENTIAL DISTRIBUTION PARAMETERS
... third modified moment estimators (MME-III) is given better accuracy as compared to previously defined traditional method of moments (ME) and first modified moment estimators (MME-I) for small samples of ... See full document
16
Some aspects of progeny testing Southdown rams : a thesis presented in part fulfilment of the requirements for the degree of Master of Agricultural Science in Massey University of Manawatu, New Zealand
... Least squares means, least squares deviations of sire groups from the means, least squares differences due to birthrank and sex, and partial regression coefficients for the characteristi[r] ... See full document
14
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] ... See full document
10
Sparse kernel regression modelling using combined locally regularized orthogonal least squares and D optimality experimental design
... sparse regression models. If data are highly noisy, however, the parsimonious principle alone may not be entirely immune to over fitting, and small models con- structed may still fit into ...[6]–[8]. ... See full document
8
Automatic kernel regression modelling using combined leave one out test score and regularised orthogonal least squares
... Clearly, the amount of computation is significantly reduced by using (15), in which no matrix inversion is involved. This is due to the fact that the calculation of the PRESS error is now based on an orthogonalised model ... See full document
18
Orthogonal Least Square with Boosting for Regression
... sparse regression models based on the orthogonal least square method with ...append regressors one by one in an orthogonal forward selection ... See full document
6
Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability
... cept of cross validation [6], it is highly desirable to develop model selective criteria based on the concept of cross vali- dation that can distinguish model generalization capability during the model construction ... See full document
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