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Additionally, PDF estimators based on kernel functions are also developed.
Weights are assigned based on a kernel size, which can be either fixed or adaptive.
However, some other feature extraction methods have put forward new ways which are based on kernels.
Depending on kernel size and p value, the total number of statistically significant CISs varies.
To this end, we first define the local kernel alignment based on centralized kernel alignment.
A kernel optimization method based on fusion kernel for high-resolution range profile (HRRP) is proposed in this paper.
Rates vary based on yard size.
Then, the MSVM is constructed based on Gauss kernel support vector machine (SVM) and polynomial kernel SVM.
Because of the uneven sampling of populations across Europe, we developed an approach based on Gaussian kernel interpolation that allows for an adaptive kernel width.
A kernel method is a weighting scheme based on a kernel function (e.g. uniform, Epanechnikov, and Gaussian).
To identify the location and extent of GRRs, we used the kernel density estimation, a non-parametric technique based on the kernel density function [ 88].
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