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The expression for each detection algorithm is then derived in the feature space, which is kernelized in terms of the kernel functions in order to avoid explicit computation in the high-dimensional feature space.
This measure can be explicitly written in terms of the kernel τ t and the filter πt−1.
The equation is written in terms of the kernel, which contains the differential cross section and represents a singularity.
In terms of the kernel of a linear system Györi and Reynolds [10] found necessary conditions for the solutions to be bounded.
Roughly speaking, the components of the higher-dimensional vector correspond to terms of the kernel function, if it were expanded out as a sum of products of the features of the original, unexpanded pair of vectors.
Therefore, we use two convolutional layers in this paper; in terms of the kernel size, the results for a 3 × 1 kernel size are nearly identical to those for a 5 × 1 kernel size.
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Motivated by this, we consider a parametric version of the kernel function in [14] with parameters in the barrier term of the kernel function.
Wang-Bai [16] defined a parametric version of the kernel function in [14], the parameter is in the growth term of the kernel function, and generalized the algorithm for LO to P ∗ -LCPs based on this kernel function.
Motivated by this, we introduce a parameter in the barrier term of the kernel function in [14] and obtained the best known complexity result for large-update methods for all parameters.
One class of Kato class measures is defined by way of the heat kernel, another is defined in terms of the Green kernel depending on some exponents related to the heat kernel estimates.
However, this apparent drawback allows us to neglect the normalization term of the kernel-based 1D Gaussian probability density.
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