Results 251 to 260 of about 1,663,953 (297)

PSO-based parameter optimization of intuitionistic fuzzy generator for low-light image enhancement. [PDF]

Front Artif Intell
S UM, S J.
europepmc   +1 more source

PruDensNet: a parameter efficient depthwise separable CNN for MRI-based brain tumor classification. [PDF]

Front Med (Lausanne)
Arman M, Samin A, Islam AKMM, Arnob MMR, Islam MJ, Mamoon IA.   +5 more
europepmc   +1 more source

Evaluating non-linear thresholds and synergistic interactions in the comorbidity of preoperative insomnia and emergence agitation: a SHAP-based machine learning study. [PDF]

Front Neurol
Zhao J, Yu X, Zhang B, Wang M, Yang X.
europepmc   +1 more source

Bayesian Model Selection for Derived Responses. [PDF]

Stat Med
Roy B, Lesaffre E.
europepmc   +1 more source

A note on smoothing parameter selection for penalized spline smoothing

Journal of Statistical Planning and Inference, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kauermann, Göran
openaire   +2 more sources

Smoothing parameter selection for nonparametric regression using smoothing spline

, 2013
In this paper, the smoothing parameter selection problem has been examined in respect to a smoothing spline implementation in predicting nonparametric regression models. For this purpose, a simulation study has been performed by using a program written in MATLAB.
Aydin, Dursun, Memmedli, Memmedaga, Omay, Rabia Ece   +2 more
openaire   +3 more sources

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Electrical parameters of smooth muscle cells

American Journal of Physiology-Legacy Content, 1963
Spikes in circular intestinal muscle fibers show volume-conductor shapes and conduct faster in low resistance medium than in air. Longitudinal space constant is 1.0 mm (20 cell lengths if 50% overlap); transverse space constant is 0.27 mm or 50 cell widths.
T, NAGAI, C L, PROSSER
openaire   +2 more sources

Smoothing parameter selection for smoothing splines: a simulation study

Computational Statistics & Data Analysis, 2003
Smoothing splines are a popular method for performing nonparametric regression. Most important in the implementation of this method is the choice of the smoothing parameter. This article provides a simulation study of several smoothing parameter selection methods, including two so-called risk estimation methods.
openaire   +2 more sources

Smoothed eigenspace-based parameter estimation

Automatica, 1994
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hamid Krim, John G. Proakis
openaire   +3 more sources

Choosing the Smoothing Parameter

2001
We have now come to just about the most important aspect of nonparametric density estimation: choosing the smoothing parameter in kernel estimation that will give near-optimal results for large classes of densities.
P. P. B. Eggermont, V. N. LaRiccia
openaire   +1 more source