Results 211 to 220 of about 126,417 (256)

Construction and Validation of a Risk Prediction Model for Postoperative Lower Extremity Deep Vein Thrombosis in Patients with Vascular Access Devices: A Retrospective Analysis. [PDF]

Vasc Health Risk Manag
Sun Y, Luo M.
europepmc   +1 more source

Clinical Phenotype Comparison in Polish Patient Cohorts with and Without Molecular Diagnosis of Dystonia. [PDF]

J Clin Med
Milanowski L, Jurek M, Salińska A, Podwysocka A, Figura M, Szlufik S, Geremek M, Nowak J, Szczałuba K, Hoffman-Zacharska D, Koziorowski D.   +10 more
europepmc   +1 more source

Prediction of cognitive performance by demographics, sleep, and brain morphometry: machine learning findings from ENIGMA-Sleep Working Group

Tahmasian M, Bi H, Åkerstedt T, Bülow R, Deantoni M, Drzezga A, Elman J, Elmenhorst D, Elmenhorst E, Ewert R, Fennema-Notestine C, Ferrarelli F, Frenzel S, Von Gall C, Genon S, Grabe H, Ho PTN, Hoepel S, Hoffstaedter F, Ibáñez A, Jahanshad N, Keihani A, Küppers V, Liu W, Mayeli A, Mortazavi N, Neitzel J, Nilsonne G, Panizzon M, Rupp J, Saberi A, Schmidt C, Spiegelhalder K, Stubbe B, Tamm S, Thomopoulos S, Thompson P, Valk S, Vandewalle G, Vo-Eckerle TT, Völzke H, Waite L, Wexler J, Wittfeld K, Patil K, Weihs A, Eickhoff S, Raimondo F.   +47 more
europepmc   +1 more source

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A note on generalized cross-validation with replicates

Statistics and Probability Letters, 1992
Abstract Generalized cross-validation (GCV) is a popular method for choosing the smoothing parameter in generalized spline smoothing when there are independent errors with common unknown variance. When data points are replicated, one can choose the smoothing parameter by minimizing one of three functions: the GCV score computed from the averaged ...
Chong Gu, Nancy E Heckman, Grace Wahba
exaly   +3 more sources

Generalized Cross-Validation for Large-Scale Problems

Journal of Computational and Graphical Statistics, 1997
Abstract Although generalized cross-validation is a popular tool for calculating a regularization parameter, it has been rarely applied to large-scale problems until recently. A major difficulty lies in the evaluation of the cross-validation function that requires the calculation of the trace of an inverse matrix. In the last few years stochastic trace
Gene H Golub
exaly   +2 more sources

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, Marcotte Denis
exaly   +2 more sources

Generalized cross validation for wavelet thresholding

Signal Processing, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Maarten Jansen, Maurits Malfait, Adhemar Bultheel   +2 more
openaire   +4 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

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   +1 more source