Results 261 to 270 of about 5,783,771 (297)

Generalized cross validation for wavelet thresholding

Signal Processing, 1997
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Maarten Jansen, Maurits Malfait, Adhemar Bultheel   +2 more
openaire   +4 more sources

Improving ESVM with Generalized Cross-Validation

2015 International Joint Conference on Neural Networks (IJCNN), 2015
ELM works for the “generalized” singlehidden layer feedforward networks (SLFNs) but the hidden layer (or called feature mapping) in ELM needs not be tuned. Extreme Support Vector Machine (ESVM), combining Support Vector Machine (SVM) and Extreme Learning Machine (ELM) kernels, can lead to a better prediction capability.
Tianshu Feng, Fuzhen Zhuang, Qing He 0003   +2 more
openaire   +2 more sources

Generalized Cross Validation in variable selection with and without shrinkage [PDF]

Journal of Statistical Planning and Inference, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Maarten Jansen
exaly   +3 more sources

Generalized Cross Validation for Multiwavelet Shrinkage

IEEE Signal Processing Letters, 2004
Traditional multiwavelet shrinkage denoising techniques require a priori knowledge of noise variance that may not be obtained in some practical situations. By using generalized cross validation (GCV), we propose in this paper a new level-dependent risk estimator for multiwavelet shrinkage that does not require such a priori information.
Tai-Chiu Hsung, Daniel Pak-Kong Lun
openaire   +1 more source

On Generalized Cross Validation for Tensor Smoothing Splines

SIAM Journal on Scientific and Statistical Computing, 1990
The natural tensor-product smoothing spline is one of the methods of choice for fitting noisy data given on a grid. A generalized cross-validation procedure for automatic selection of the smoothing parameter in the method is introduced. It is shown that as in the well-known univariate and thin plate spline cases, the method selects the parameter in an ...
Larry L. Schumaker, Florencio I. Utreras
openaire   +1 more source

Blur identification by the method of generalized cross-validation

IEEE Transactions on Image Processing, 1992
The point spread function (PSF) of a blurred image is often unknown a priori; the blur must first be identified from the degraded image data before restoring the image. Generalized cross-validation (GCV) is introduced to address the blur identification problem.
Stanley J. Reeves, Russell M. Mersereau
openaire   +3 more sources

Cross-Validating Non-Gaussian Data: Generalized Approximate Cross-Validation Revisited

Journal of Computational and Graphical Statistics, 2001
This article presents an alternative derivation of the generalized approximate crossvalidation (GACV) score of Xiang and Wahba (1996) for smoothing parameter selection in penalized likelihood regression. The new derivation suggests a simple numerical solution that is stable for all sample sizes.
Chong Gu, Dong Xiang
openaire   +1 more source

Generalized cross-validation for covariance model selection

Mathematical Geosciences, 1995
A weighted cross-validation technique known in the spline literature as generalized cross-validation (GCV), is proposed for covariance model selection and parameter estimation. Weights for prediction errors are selected to give more importance to a cluster of points than isolated points.
Denis Marcotte
exaly   +2 more sources

Wavelet shrinkage and generalized cross validation for image denoising

IEEE Transactions on Image Processing, 1998
We present a denoising method based on wavelets and generalized cross validation and apply these methods to image denoising. We describe the method of modified wavelet reconstruction and show that the related shrinkage parameter vector can be chosen without prior knowledge of the noise variance by using the method of generalized cross validation.
Norman Weyrich, Gregory T. Warhola
openaire   +3 more sources

Efficient generalized cross-validation for state space models

Biometrika, 1987
The initial model considered is \(y(i)=s(i)+e(i)\), \(i=1,...,n\), where s(i) is an unobserved Gaussian signal and the e(i) are independent \(N(0,\sigma^ 2)\) and independent of s(i). The s(i) are generated by the state space model \[ (*)\quad s(i)=h(i,\theta)'x(i),\quad x(i+1)=F(i,\theta)x(i)+u(i) \] where u(i) is a sequence of q-dimensional ...
Ansley, Craig F., Kohn, Robert
openaire   +2 more sources