Results 41 to 50 of about 257,461 (188)

Iterative graph cuts for image segmentation with a nonlinear statistical shape prior

, 2013
Shape-based regularization has proven to be a useful method for delineating objects within noisy images where one has prior knowledge of the shape of the targeted object.
A. O’Hagan, B. Silverman, D. Cremers, D. Cremers, D. Cremers, D. Cremers, D. Freedman, D. Freedman, D. Hunter, G. Slabaugh, J. Chang, J. Malcolm, J. Malcolm, J. Schindelin, J. Zhu-Jacquot, Joshua C. Chang, L. Grady, N. El Zehiry, N. Vu, O. Veksler, P. Das, P. Kohli, P. Viola, S. Belongie, S. Dambreville, S. Lee, S. Tabbone, S.C. Pei, T. Heimann, T. Jiang, Tom Chou, V. Lempitsky, V. Lempitsky, V. Vineet, Y. Boykov, Y. Boykov, Y. Boykov   +36 more
core   +1 more source

Fault Diagnosis of Rolling Bearings Based on EWT and KDEC

Entropy, 2017
This study proposes a novel fault diagnosis method that is based on empirical wavelet transform (EWT) and kernel density estimation classifier (KDEC), which can well diagnose fault type of the rolling element bearings.
Mingtao Ge, Jie Wang, Xiangyang Ren
doaj   +1 more source

Graph Bundling by Kernel Density Estimation [PDF]

Computer Graphics Forum, 2012
AbstractWe present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved graph edges into the height gradient flow ...
Hurter, Christophe, Ersoy, Ozan, Telea, Alexandru C   +2 more
openaire   +3 more sources

Multivariate mixed kernel density estimators and their application in machine learning for classification of biological objects based on spectral measurements [PDF]

Компьютерная оптика, 2019
A problem of non-parametric multivariate density estimation for machine learning and data augmentation is considered. A new mixed density estimation method based on calculating the convolution of independently obtained kernel density estimates for ...
Alexander Sirota, Artem Donskikh, Alexey Akimov, Dmitry Minakov   +3 more
doaj   +1 more source

A new family of kernels from the beta polynomial kernels with applications in density estimation

IJAIN (International Journal of Advances in Intelligent Informatics), 2020
One of the fundamental data analytics tools in statistical estimation is the non-parametric kernel method that involves probability estimates production.
Israel Uzuazor Siloko, Wilson Nwankwo, Edith Akpevwe Siloko   +2 more
doaj   +1 more source

Kernel density estimation with Berkson error [PDF]

Canadian Journal of Statistics, 2016
AbstractGiven a sample from we construct kernel density estimators for , the convolution of with a known error density . This problem is known as density estimation with Berkson error and has applications in epidemiology and astronomy. Little is understood about bandwidth selection for Berkson density estimation.
Long, James P., El Karoui, Noureddine, Rice, John A.   +2 more
openaire   +2 more sources

Optimal Bandwidth Selection for Kernel Density Functionals Estimation

Journal of Probability and Statistics, 2015
The choice of bandwidth is crucial to the kernel density estimation (KDE) and kernel based regression. Various bandwidth selection methods for KDE and local least square regression have been developed in the past decade.
Su Chen
doaj   +1 more source

What Do Kernel Density Estimators Optimize? [PDF]

Journal of Econometric Methods, 2012
Summary: Some linkages between kernel and penalty methods of density estimation are explored. It is recalled that classical Gaussian kernel density estimation can be viewed as the solution of the heat equation with initial condition given by data. We then observe that there is a direct relationship between the kernel method and a particular penalty ...
Koenker, Roger, Mizera, Ivan, Yoon, Jungmo   +2 more
openaire   +2 more sources

Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors

Econometrics, 2016
This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density.
Xibin Zhang, Maxwell L. King, Han Lin Shang   +2 more
doaj   +1 more source

Improving Radio Source Count Estimation Using Kernel Density Estimation

The Astrophysical Journal
Radio source counts provide a fundamental census of cosmic radio emission, yet their estimation is usually based on coarse histograms that suffer from bin-choice bias, boundary effects, and survey incompleteness.
Luozhenhan Liu, Zunli Yuan, Wenjie Wang, Chuanqi Li   +3 more
doaj   +1 more source