1,721,010 research outputs found
Orthogonal least squares regression with tunable kernels
Full text linkA novel technique is proposed to construct sparse regression models based on the orthogonal least squares method with tunable kernels. The proposed technique tunes the centre vector and diagonal covariance matrix of individual regressor by incrementally minimising the training mean square error using a guided random search algorithm, and it offers a state-of-the-art method for constructing very sparse models that generalise well
Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model
Full text linkUsing the correlation criterion to position and shape RBF units for incremental modelling
No full textA novel technique is proposed for the incremental construction of sparse radial basis function (RBF) networks. The correlation between a RBF regressor and the training data is used as the criterion to position and shape the RBF node, and it is shown that this is equivalent to incrementally minimise the modelling mean square error. A guided random search optimisation method, called the repeated weighted boosting search, is adopted to append RBF nodes one by one in an incremental regression modelling procedure. The experimental results obtained using the proposed method demonstrate that it provides a viable alternative to the existing state-of-the-art modelling techniques for constructing parsimonious RBF models that generalise well
Parsimonious support vector machine regression using orthogonal forward selection with the generalized kernel model
No full textSparse regression modelling using an incremental weighted optimization method based on boosting with correlation criterion
No full textA novel technique is presented to construct sparse Gaussian regression models. Unlike most kernel regression modelling methods, which restrict kernel means to the training input data and use a fixed common variance for all the regressors, the proposed technique can tune the mean vector and diagonal covariance matrix of individual Gaussian regressor to best fit the training data based on the correlation between the regressor and the training data. An efficient repeated weighted optimization method is developed based on boosting with the correlation criterion to append regressors one by one in incremental regression modelling. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-art kernel modelling methods for constructing parsimonious regression models
Experiments with repeating weighted boosting search for optimization in signal processing applications
No full textAn approach for constructing parsimonious generalized Gaussian kernel regression models
No full textThe paper proposes a novel construction algorithm for generalized Gaussian kernel regression models. Each kernel regressor in the generalized Gaussian kernel regression model has an individual diagonal covariance matrix, which is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. The standard orthogonal least squares algorithm is then used to select a sparse generalized kernel regression model from the resulting full regression matrix. Experimental results involving two real data sets demonstrate the effectiveness of the proposed regression modeling approach
NARX-based nonlinear system identification using orthogonal least squares basis hunting
Full text linkAn orthogonal least squares technique for basis hunting (OLS-BH) is proposed to construct sparse radial basis function (RBF) models for NARX-type nonlinear systems. Unlike most of the existing RBF or kernel modelling methods, which places the RBF or kernel centers at the training input data points and use a fixed common variance for all the regressors, the proposed OLS-BH technique tunes the RBF center and diagonal covariance matrix of individual regressor by minimizing the training mean square error. An efficient optimization method is adopted for this basis hunting to select regressors in an orthogonal forward selection procedure. Experimental results obtained using this OLS-BH technique demonstrate that it offers a state-of-the-art method for constructing parsimonious RBF models with excellent generalization performance
Sparse support vector regression based on orthogonal forward selection for the generalised kernel model
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