Results 1  10
of
442,199
Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability
"... The paper introduces a construction algorithm for sparse kernel modelling using the leaveoneout test score also known as the PRESS (Predicted REsidual Sums of Squares) statistic. An efficient subset model selection procedure is developed in the orthogonal forward regression framework by incrementa ..."
Abstract
 Add to MetaCart
The paper introduces a construction algorithm for sparse kernel modelling using the leaveoneout test score also known as the PRESS (Predicted REsidual Sums of Squares) statistic. An efficient subset model selection procedure is developed in the orthogonal forward regression framework
Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability
"... The paper introduces a construction algorithm for sparse kernel modelling using the leaveoneout test score also known as the PRESS (Predicted REsidual Sums of Squares) statistic. An efficient subset model selection procedure is developed in the orthogonal forward regression framework by incrementa ..."
Abstract
 Add to MetaCart
The paper introduces a construction algorithm for sparse kernel modelling using the leaveoneout test score also known as the PRESS (Predicted REsidual Sums of Squares) statistic. An efficient subset model selection procedure is developed in the orthogonal forward regression framework
Sparse modelling using orthogonal forward regression with press statistic and regularization
 IEEE TRANS. SYSTEMS, MAN AND CYBERNETICS, PART B
, 2004
"... The paper introduces an efficient construction algorithm for obtaining sparse linearintheweights regression models based on an approach of directly optimizing model generalization capability. This is achieved by utilizing the delete1 cross validation concept and the associated leaveoneout tes ..."
Abstract

Cited by 48 (23 self)
 Add to MetaCart
oneout test error also known as the predicted residual sums of squares (PRESS) statistic, without resorting to any other validation data set for model evaluation in the model construction process. Computational efficiency is ensured using an orthogonal forward regression, but the algorithm incrementally
MEstimator and DOptimality Model Construction Using Orthogonal Forward Regression
"... Abstract—This correspondence introduces a new orthogonal forward regression (OFR) model identification algorithm using Doptimality for model structure selection and is based on an Mestimators of parameter estimates. Mestimator is a classical robust parameter estimation technique to tackle bad dat ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
Abstract—This correspondence introduces a new orthogonal forward regression (OFR) model identification algorithm using Doptimality for model structure selection and is based on an Mestimators of parameter estimates. Mestimator is a classical robust parameter estimation technique to tackle bad
Probability Density Function Estimation Using Orthogonal Forward Regression
"... Abstract — Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a ..."
Abstract
 Add to MetaCart
Abstract — Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises
Particle swarm optimization aided orthogonal forward regression for unified data modelling
 IEEE TRANS. EVOLUTION. COMPUT
, 2010
"... We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave ..."
Abstract

Cited by 8 (4 self)
 Add to MetaCart
We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based
AN ORTHOGONAL FORWARD REGRESSION ALGORITHM COMBINED WITH BASIS PURSUIT AND DOPTIMALITY
"... A new forward regression model identification algorithm is introduced. The derived model parameters, in each forward regression step, are initially estimated via orthogonal least squares (OLS) (using the modified GramSchmidt procedure), followed by being tuned with a new gradient descent learning ..."
Abstract
 Add to MetaCart
A new forward regression model identification algorithm is introduced. The derived model parameters, in each forward regression step, are initially estimated via orthogonal least squares (OLS) (using the modified GramSchmidt procedure), followed by being tuned with a new gradient descent learning
AOptimality Orthogonal Forward Regression Algorithm Using Branch and Bound
"... sional data by mixtures of factor analyzers, ” Comput. Statist. Data ..."
2.1 Generalized Orthogonal Forward Regression GOFR is an extension of the Orthogonal Forward Regression algorithm originally designed for regression and feature selection. Given a signal...
"... We propose a new approach to fully automatic ECG wave extraction and morphology tracking. It is based on Generalized Orthogonal Forward Regression (GOFR), which allows decomposing a onedimensional signal into a set of appropriate parameterized functions. Two applications of GOFR to ECG modeling are ..."
Abstract
 Add to MetaCart
We propose a new approach to fully automatic ECG wave extraction and morphology tracking. It is based on Generalized Orthogonal Forward Regression (GOFR), which allows decomposing a onedimensional signal into a set of appropriate parameterized functions. Two applications of GOFR to ECG modeling
Sparse kernel density construction using orthogonal forward regression with leaveoneout test score and local regularization
 IEEE Trans. Systems, Man and Cybernetics, Part B
, 2004
"... An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimi ..."
Abstract

Cited by 16 (7 self)
 Add to MetaCart
An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally
Results 1  10
of
442,199