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Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network.
Based on kernel theory, each element of kernel matrix K = [k ij ] ∈ ℝN × N is as follows: k ij = φ x i, φ x j = φ x i T φ x j (9).
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In the paper, we presented a new computational framework based on kernel sparse representation theory to predict S-nitrosylation sites.
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.
In this paper, based on reproducing kernel theory, reproducing kernels with polynomial form will be constructed and a computational method is described in order to obtain the accurate numerical solution with polynomial form of equation (1) in the reproducing kernel spaces spanned by the Chebyshev basis polynomials.
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.
To study these effects, we perform numerical simulations of the bicomponent population balance equation using a constant aggregation kernel as well as a kernel based on the kinetic theory of granular flow (KTGF).
Finally, the proposed WF kernel based on Markov chain theory for genotype data has been proven useful compared with some existing kernels and can potentially benefit a wide range of kernel methods, such as kernel PCA, support vector machines and nonparametric and semiparametric regressions.
It is known that in the field of learning theory based on reproducing kernel Hilbert spaces the upper bounds estimate for a -functional is needed.
To this end, we first define the local kernel alignment based on centralized kernel alignment.
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