Sentence examples for kernel output from inspiring English sources

Such redundancy arises from the fact that coefficients with permutated indices (e.g., h 2 0,1) and h 2(1,0)) are multiplied by the same cross product of the input signal (e.g., x(n)x(n−1) in the case of h 2 0,1) and h 2(1,0)) when the kernel output is evaluated.

If the value of σ is too small-that is, most pairs of vectors are far enough apart that the kernel output is near zero, the SVM will have too little information to make an accurate classification.

A kernel is active, with an output value of 1, as soon as the 4D-joint angle vector falls into the topographical region covered by the kernel, otherwise kernel output is zero.

It is clear from Figures 6 and 7 that the distance at which the kernel output reaches approximately zero varies with σ, and therefore the choice of σ for this kernel is essential in properly distinguishing the level of similarity between two input vectors.

This restricts the range of the dot product in (12) to ±1, yielding kernel outputs between 0 and 2 d, where d is the degree of the polynomial.

The core takes in 13 parallel 8-bit input from a 5 × 5 kernel to generate the core outputs.

An operator (matrix -valued kernel (as output space is, the operator is a linear application on vectors of and thus a matrix -valued properties can be found in Senkernelnd Tempel'mas (1973), takes intoutputunt the spacearisy betheen twoperators of is a much richer way than a scalinearlued kernel, applicationxt.

While Fig. 5a f show kernels and output raster images of spatial filters.

Section 2 revisits the Volterra filters, discussing the redundancy-removed and matrix-form representations of kernel input-output relationships.

Such representation of the kernel input-output relationship, known as triangular [1] or even redundancy-removed [15] representation, results in a number of coefficients given by underline{N}_{p} = frac{(N+p-1)!}{ N-1)!p!}.

Each of the machine-learning models was an ensemble of 400 support vector machines (SVMs) 21 with linear kernel (ie, the output of the model was the average of 400 SVM outputs in Platt's a posteriori probabilities 22).