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Several functions are constructed for evaluating the significance of features based on kernel approximation and fuzzy entropy.
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Additionally, PDF estimators based on kernel functions are also developed.
However, some other feature extraction methods have put forward new ways which are based on kernels.
Based on the reproducing kernel approximation, the domain of interest is discretized by a set of particles without the employment of a structured mesh, which constitutes an advantage over the finite element method.
The potential is determined by solving a Dirichlet problem and evaluation of the single layer potential by a fast approximation technique based on Fourier approximation of the kernel function.
Based on reproducing kernel theory, reproducing kernel functions with polynomial form will be constructed in the reproducing kernel spaces spanned by the Chebyshev basis polynomials.
A method for the solution of Fredholm integral equations of the second kind with sigularities both in the kernel and in the solution is developed, based on the approximation of the solution by B-splines.
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.
Most price indexes are based on some approximation to such a sampling design.
The method is based on an approximation result that we called the 'A-caloric approximation lemma'.
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