Results 221 to 230 of about 126,417 (256)
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Blur identification by the method of generalized cross-validation
IEEE Transactions on Image Processing, 1992The 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
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Global optimization of the generalized cross-validation criterion
Statistics and Computing, 2000Generalized cross-validation is a method for choosing the smoothing parameter in smoothing splines and related regularization problems. This method requires the global minimization of the generalized cross-validation function. In this paper an algorithm based on interval analysis is presented to find the globally optimal value for the smoothing ...
John T. Kent, Mohsen Mohammadzadeh
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On Generalized Cross Validation for Tensor Smoothing Splines
SIAM Journal on Scientific and Statistical Computing, 1990The 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
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Cross-Validating Non-Gaussian Data: Generalized Approximate Cross-Validation Revisited
Journal of Computational and Graphical Statistics, 2001This 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
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Wavelet shrinkage and generalized cross validation for image denoising
IEEE Transactions on Image Processing, 1998We 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
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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.
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Least Squares Model Averaging Based on Generalized Cross Validation
Acta Mathematicae Applicatae Sinica, English Series, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Xin-min +3 more
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Efficient generalized cross-validation for state space models
Biometrika, 1987The 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
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General Approximate Cross Validation for Model Selection
Proceedings of the 29th ACM International Conference on Multimedia, 2021Cross-validation (CV) is a ubiquitous model-agnostic tool for assessing the error of machine learning. However, it has high complexity due to the requirement of multiple times of learner training especially in multimedia tasks with huge amounts of data.
Bowei Zhu, Yong Liu 0018
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Fast Generalized Cross-Validation Algorithm for Sparse Model Learning
Neural Computation, 2007We propose a fast, incremental algorithm for designing linear regression models. The proposed algorithm generates a sparse model by optimizing multiple smoothing parameters using the generalized cross-validation approach. The performances on synthetic and real-world data sets are compared with other incremental algorithms such as Tipping and Faul's ...
Sundararajan, S +2 more
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