Exact(2)
Let (K x)) be a kernel function.
Let K be a kernel function in R and satisfy the following condition (A1): (Assumesume that K is a bounded probability density function and K ∈ H s, where H s is a class of functions K with the properties ∫ − ∞ ∞ u r K ( u ) d u = 0, r = 1, 2, …, s − 1, ∫ − ∞ ∞ u s K ( u ) d u = A ≠ 0. (2.2).
Similar(58)
where K σ (z) is a kernel function.
Here, K is a kernel function and h is the bandwidth.
where G is a kernel function, P ^ is the estimated propensity score, and a N is a bandwidth.
where α i are Lagrange multipliers of the dual optimization problem, K(x i,x) is a kernel function and b is a bias or threshold parameter.
where α rij is a coefficient, x ij and x lk are n-dimensional column vectors, and k(x ij, x lk ) is a kernel function.
where is a kernel function, is the bandwidth of the kernel, is the dimension of, means the number of pixels in, and the label equals 0 for the background or 1 for the object.
The second column is the kernel function of each isotropic kernel where K x) is a kernel function versus varaible x and the third and fourth column are shape and performance of isotropic kernels, respectively.The efficiency of estimator will evaluate with mean square error (MSE) [41].
where and are vectors defined on normalized values of power spectral density of the window of the first and second cells, respectively, (see Figure 2), and is a kernel function.
For Ψ one typically has the following choices: ( varPsi left x,{x}_kright)={x}_k^Tx ) (linear SVM) and Ψ x, x k ) is a kernel function similar to KPCA.
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