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Covariance matrix computing is the most time-consuming component in target detection, so a simple and effective computing method is very important for real-time processing.
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Mean, standard deviation and correlation with G BASE for all genomic matrices computed are reported in Table S1 [See Additional file 1: Table S1].
Based on this matrixing network, distributed computing was employed in solving the huge mathematical models.
Operational matrices, which we have computed, are exact.
For such a region, the code in lines 5-8 of COMPUTE-MATRIX is executed.
The matrix square is computed through a proposed fast algorithm designed specifically for tridiagonal matrices.
The third-order matrix product is computed in the algorithm.
Hence, one matrix inversion is computed for all symbols.
Next, the observed covariance matrix S is computed directly from the variable values.
The system matrix ℳ is computed once using these baseline values.
In the training, each member in the matrix kernel is computed by applying the MLRBF function over all document vectors.
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