… In this paper, the kernel parameter, the number of k -nearest samples in the similarity matrix
W , is … 8, we can see the sampling results different from each other in geometrical shape. …

… and sample size used. Using 3 different estimators (minimum convex polygons, fixed-kernels,
clusters)… because of bad satellite geometry. A previous study confirmed the performance of …

… that are required from Riemannian geometry, including the heat kernel for a general …
Then {xv}v∈V is the sample space of the multinomial distribution, with a document modeled …

… kernel classifiers and regression methods, they only provide feature ranking and nothing is
said about the geometrical … as a sum of kernel functions defined on the training samples, (18) …

… ‐sampled superimage from a set of undersampled images can be difficult if the geometric …
, which is the theoretically appropriate sampling kernel for well‐sampled data. Last, I …

… equally, regardless of their distance to the estimated point x (kernel function is ‘flat’). Third,
one might need a larger number of samples in order to obtain accurate estimates of the …

… propagates through the geometric correction process and affects … Sampling Control Points:
We created a sampling design for a … We therefore used this kernel width for the ruggedness …

… function value of samples and the geometric mean value of the samples is established. Then,
… pointwise correction coefficient is determined to modify bandwidth. The final kernel density …

… The orientation of geometrically corrected images, however, artificially introduces zero-reflectance
background pixels. These change the variance–covariance structure of the image …

… geometric sensitivity, photon pair non-colinearity, attenuation, intrinsic detector sensitivity,
non-uniform sinogram sampling, … We then truncate the kernel at 1% of its maximum value to …