orthogonal least squares
Sparse kernel regression modelling using combined locally regularized orthogonal least squares and D optimality experimental design
... ized orthogonal least squares (LROLS) algorithm [10]–[12] has re- cently been proposed, which introduces an individual regularizer for each ...
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Local Regularization Assisted Orthogonal Least Squares Regression
... The orthogonal least squares (OLS) algorithm [9,11] is an efficient learning procedure for constructing sparse regression ...every orthogonal weights in the ...
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A multiple sequential orthogonal least squares algorithm for feature ranking and subset selection
... This study introduces a new method for ranking significant variables and selecting a subset from a library consisting of all the original variables. In the new method, a general variable detection and subset selection ...
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Finite impulse response filter design using a forward orthogonal least squares algorithm
... standard least squares problem. Compared with simple least squares algorithms, the orthogonal least squares method has been demonstrated to be a powerful means to achieve ...
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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 ...forward orthogonal regression (OFR) algorithm, ...
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Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks
... regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, ...
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Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems
... associated Orthogonal Forward Regression (OFR) algorithm (Billings, 2013) (also referred to as the OLS (Orthogonal Least Squares) or the FOLSR (Forward Orthogonal Least ...
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Automatic kernel regression modelling using combined leave one out test score and regularised orthogonal least squares
... of Squares) statistic and regularised orthogonal least ...regularised orthogonal weight model, develop an efficient recursive computation formula for PRESS errors in the regularised ...
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Orthogonal least squares regression with tunable kernels
... an orthogonal forward selection procedure using a guided random search algorithm, called the repeated weighted boosting search (RWBS) ...novel orthogonal least squares (OLS) algorithm with ...
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Model structure selection in powertrain calibration and control
... In this section, a one dimensional scheduled multi-model is developed for a 4 cylinder Zetec 1.6L PFI gasoline engine. MIMO linear affine and nonlinear affine models are adopted as local models which can be developed ...
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Sparse model identification using orthogonal forward regression with basis pursuit and D optimality
... the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l 1 norm of the parameter estimate ...
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Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model
... the Orthogonal Least Squares (OLS) algorithm (Chen et ...The Least Squares Support Vector Machine (LS-SVM) regression (de Kruif and de Vries, 2003; Suykens and Van-dewalle, 1999; ...
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On weighted structured total least squares
... is a solution technique for an overdetermined system of equations AX ≈ B, A ∈ IR m × n , B ∈ IR m × d . It is a natural generalization of the least squares approximation method when the data in both A and B ...
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Consistent least squares fitting of ellipsoids
... We point out several papers in which the ellipsoid fitting problem is considered. Gander et. al. [GGS94] consider algebraic and geometric fit- ting methods for circles and ellipses and note the inadequacy of the alge- ...
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Least squares approximations of power series
... classical least squares solutions in C[ − 1, 1] in terms of linear combinations of ul- traspherical polynomials are extended in order to estimate power series on ( − 1, ...
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Overview of total least squares methods
... total least squares problems [12], the data matrix A B is ...total least squares problem formulation is ex- tended [31] with the additional constraint that the structure of the data matrix A B ...
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Liquid Liquid Extraction and Simultaneous Spectrophotometric Determination of Nickel and Cobalt by PAR with Orthogonal Signal Correction Partial Least Squares
... Abstract—Partial least squares modeling is a powerful multivariate statistical tool applied to extraction spectrophotometric simultaneous determination of mixtures of nickel and ...partial least ...
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Characterization and optimization of bilosomes for oral vaccine delivery
... partial least squares (PLS) orthogonal coefficients of the bilosome composition on pH, zeta potential and vesicle size are shown in Fig 1A to C, respectively; these results show three significant ...
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Orthogonal Least Square with Boosting for Regression
... A novel technique is presented to construct sparse regression models based on the orthogonal least square method with boosting. This technique tunes the mean vector and diagonal covariance matrix of ...
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Approximate Least Squares Accelerator
... with least significant bits of other 2x2 blocks to enable more compression and reduce the height of the partial product matrix by a factor of ...the least ap- proximate design of this method has a very ...
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