orthogonal least
Sparse kernel regression modelling using combined locally regularized orthogonal least squares and D optimality experimental design
... [5] is an efficient learning procedure for constructing 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- ...
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Tree Based Orthogonal Least Squares Regression with Repeated Weighted Boosting Search
... In order to a sparse representation, this paper proposes a novel tree based orthogonal least squares regression. Unlike most of the conventional OLSR, the new method keeps the k excellent regressors which ...
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Automatic kernel regression modelling using combined leave one out test score and regularised orthogonal least squares
... regularised orthogonal least ...regularised orthogonal weight model, develop an efficient recursive computation formula for PRESS errors in the regularised orthogonal least squares ...
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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, the ...
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Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks
... Simpler learning can often be achieved if a neural network has a linear-in-the-parameters structure. When the width pa- rameter is fixed and a set of RBF centers is provided, a RBF network has such a structure and an ...
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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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Finite impulse response filter design using a forward orthogonal least squares algorithm
... Abstract— This paper is concerned with the application of forward Orthogonal Least Squares (OLS) algorithm to the design of Finite Impulse Response (FIR) filters. The focus of this study is a new FIR filter ...
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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 tunable ...
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A multiple sequential orthogonal least squares algorithm for feature ranking and subset selection
... encountered in multiple regression and multivariate pattern recognition. It has been noted that in many cases not all the original variables are necessary for characterizing the overall features. More often only a subset ...
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Local Regularization Assisted Orthogonal Least Squares Regression
... regularized orthogonal least squares (LROLS) algorithm is proposed for constructing parsimonious or sparse regression models that generalize ...each orthogonal weight in the regression model with an ...
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Sparse multi output radial basis function network construction using combined locally regularised orthogonal least square and D Optimality experimental design
... A new construction algorithm for multi-output radial basis function (RBF) network modelling is introduce by combining a locally regularized orthogonal least squares (LROLS) model selection with a ...
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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] ...
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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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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 Squares ...
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Model structure selection in powertrain calibration and control
... and orthogonal least squares can be applied successfully on the engine identification process and produced both good polynomial ARX and NARX models for the engine ...
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An Orthogonal Forward Regression Algorithm Combined with Basis Pursuit and D Optimality
... via orthogonal least squares (OLS) (using the modified Gram-Schmidt proce- dure), followed by being tuned with a new gradient descent learning algorithm based on the basis pursuit that minimizes the norm of ...
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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; Suykens et ...the ...
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Orthogonal persistence revisited
... In the 1980s orthogonal persistence focussed on the differences between long- and short-term storage. As described above, this is just one of many mappings that an application builder needs to be concerned with; ...
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A Survey on Channel Estimation Algorithms for LTE Downlink Systems
... In LTE standard pilot symbols are inserted at specific position in each PRB. Pilot symbols are also referred as reference signals. In figure 5, arrangement of pilots and information symbols for MIMO system is shown. ...
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Improvisation to the R* Tree kNN Join Principles in Distributed Environment
... Dimensionality of Data Block Nested Loop orthogonal edges Single z-value parttioning orthogonal edges Multiple z-value partitioning orthogonal edges Figure 6 Average percentage differenc[r] ...
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