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Exact(3)
By using this exhaustive search procedure, the proposed method enables determination of the multiple kernel function.
When consider the case if the multiple kernel function is a multiple variable kernel, by the similar argument as in [3, 8], we can prove Lemma 2.2.
Then, by successfully determining the parameters γ l (l=0,1,⋯,L) in the multiple kernel function, the features can be mapped into the optimal feature space, enabling accurate ordinal regression.
Similar(57)
By comparing the performances of multiple kernel functions using cross validation on training set, radial basis function (RBF) is selected as the kernel function used in this simulation.
Given two vectors x m and x n ∈R d, our method uses the following multiple kernel functions: ϕ x m ′ ϕ x n = ∑ l = 0 L γ l κ l E l x m, E l x n, (8).
For the calculation of a large set of ODEs, the single kernel function that calculates all equations would be split into multiple kernel functions where each calculates only a small number of ODE equations.
Multiple kernel learning allows for an optimal kernel function to be learned in a computationally efficient manner.
During the process of capturing, the receiver depending on the GPU high-performance processor concurrently opens the Kernel function of multiple cores, and each Kernel function performs PRN code acquisition in a frequency domain for a satellite.
Different bioinformatic tools allow in silico predictions based on empirically derived rules for position-based residues preference (Position-Specific Scoring Matrix Methods, PSSM), Support Vector Machine (SVM) algorithms for machine learning (Linear or Radial Basis Function Kernel, Multiple Kernel Learning), or a Group-Based Prediction System (GPS) algorithm.
Thus, the proposed method extends the kernel function to a multiple kernel version as shown in Equation 8.
Hence, the recurrence is: In many applications, multiple kernels from different kernel functions or multiple sources of information are available.
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