[PDF] Top 20 Kernel density construction using orthogonal forward regression
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Kernel density construction using orthogonal forward regression
... sparse kernel density estimation using an orthogonal forward re- gression (OFR) based on leave-one-out (LOO) test score and local ...This construction algorithm is fully ... See full document
6
Sparse kernel density construction using orthogonal forward regression with leave one out test score and local regularization
... efficient construction algorithm has been presented for obtaining kernel density estimates based on an orthogonal forward regression procedure that incrementally minimizes the ... See full document
10
Sparse kernel density construction using orthogonal forward regression with leave one out test score and local regularization
... efficient construction algorithm has been presented for obtaining kernel density estimates based on an orthogonal forward regression procedure that incrementally minimizes the ... See full document
10
Sparse modelling using orthogonal forward regression with PRESS statistic and regularization
... efficient construction algo- rithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization ...model construction process. ... See full document
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An extended orthogonal forward regression algorithm for system identification using entropy
... model using the first 500 pairs of data and the extended OFR algorithm is shown in Table 5, which indicates that only 7 out of 66 candidate terms are selected to yields a very simple nonlinear ...probability ... See full document
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Fast kernel classifier construction using orthogonal forward selection to minimise leave one out misclassification rate
... respectively. Kernel classifiers were constructed over 100 training data sets and generalisation performance was evaluated using the average misclassification rate of the corresponding classifiers over the 100 ... See full document
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Fast kernel classifier construction using orthogonal forward selection to minimise leave one out misclassification rate
... Overview of Existing Methods o Nonlinear optimisation approach: Optimise all parameters kernel centre vectors, variances or covariance matrices, and weights P Very “sparse” small size P [r] ... See full document
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Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure
... It can be seen that at each incremental modeling stage, the basic task is to maximize the Fisher ratio criterion F k (u) over u 2 U, where the vector u contains the kernel mean vector and the diagonal co- variance ... See full document
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Sparse model identification using a forward orthogonal regression algorithm aided by mutual information
... models using some specific types of basis functions including polynomials, kernel basis functions, and multiresolution wavelets [3]–[6], ...linear-in-the-parameters regression models, which will be ... See full document
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Sparse model identification using orthogonal forward regression with basis pursuit and D optimality
... modelling using associative memory networks or fuzzy logic has been the problem of the curse of dimensionality ...model construction procedure to overcome the obstacle by deriving a model with an ... See full document
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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 ... See full document
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A kernel based two class classifier for imbalanced data sets
... two-class kernel classifier construction algorithm uses OFS in order to optimize the model generalization for imbalanced data ...new kernel classifier identification algorithm is based on a new ROWLS ... See full document
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An Orthogonal Forward Regression Algorithm Combined with Basis Pursuit and D Optimality
... modelling using associative memory networks or fuzzy logic has been the problem of the curse of dimensionality ...model construction procedures to overcome the obstacle by deriv- ing a model with an ... See full document
6
Automatic kernel regression modelling using combined leave one out test score and regularised orthogonal least squares
... algorithm using the leave-one-out test score also known as the PRESS (Predicted REsidual Sums of Squares) statistic and regularised orthogonal least ...ridge regression and model optimal ... See full document
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Sparse kernel regression modelling using combined locally regularized orthogonal least squares and D optimality experimental design
... size using a penalty term to penalize large sized ...in forward regression only affects the stopping point of the model selection, but does not penalizes the regressor that may cause poor model ... See full document
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Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model
... the kernel parameters based on the given training data before doing a classical LS-SVM ...generalised kernel function is used in which each kernel regressor has its tunable centre vector and diagonal ... See full document
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A sparse kernel density estimation algorithm using forward constrained regression
... probability density function (pdf) from observed data samples ...probability density function estimation is the finite mixture model ...sparse density estimators include the support vector ... See full document
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Parsimonious support vector machine regression using orthogonal forward selection with the generalized kernel model
... machine. Forward selection using the orthogonal least squares (OLS) algorithm [1] is a simple and efficient construction method that is capable of producing parsimonious linear-in-the-weights ... See full document
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Sparse support vector regression based on orthogonal forward selection for the generalised kernel model
... standard kernel regression modelling, which positions the kernel centres at the training input data points and adopts a single common variance for every kernel ... See full document
14
M estimator and D optimality model construction using orthogonal forward regression
... Alternatively there exists a vast amount of work on sparse modeling including the well-known support vector machine (SVM) [14], which is often used in classification tasks [15] and can also be used in sparse ... See full document
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