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We propose an efficient algorithm to compute the optimal value of the shape parameter that minimizes the approximation error.
Obtaining the parameters that yield the approximation of a particular function depends on a supervised learning algorithm, generally using a gradient descendent approach that iteratively minimizes the approximation error for a given dataset, but often leads to local minima [33].
The aim is to find a continuous distribution approximation ϕ, which minimizes the approximation error.
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Most of these methods focus on minimizing the approximation error; however, they usually result in a loss of physical interpretability of the reduced model.
The problem becomes looking only at the training vectors that uses only one column of the dictionary vector in its approximation, minimizing the approximation error E k.
K-SVD uses a different strategy such that the columns of A are updated sequentially one at a time by using an SVD to minimize the approximation error.
Yarn count of the thread was taken instead of the thread ticket number value to minimize the approximation errors occurring in ticket number calculations.
where,, are optimal unknown (constant) weights that minimize the approximation error over,,, are a set of basis functions such that each component of takes values between 0 and 1,,, are the modeling errors, and, where,, are bounds for the optimal weights,.
Conventional parameter estimation approaches seek parameter values that minimize the approximation error assuming a given regulatory scheme (i.e., fixing some f r,j to zero beforehand according to the aprioristic biochemical knowledge of the system).
To address this problem, we propose a novel criterion named Hilbert Space Embedding HSEE) Nyström criterion which directly minimizes the eigenvector approximation error.
The new gridding method, called LS_NUFFT, minimizes the reconstruction approximation error in the Least Square sense by generated convolution kernels that fit for the spiral k-space trajectories.
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