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The comparison convinces us that the finite difference scheme method of the second order of approximation gives better results.
Table 4 Baseline for the speaker identification experiments using the real reverberant data Experiment scheme Method Speaker identification rate P01 P03 P05 P02 P04 Avg.
Table 7 Experimental results by using combined training data (three known positions) on the first testing scheme Method Dataset Speaker identification rate P01 P03 P05 P02 P04 Avg.
The second scheme (method B) is a modification of the standard DD formula based on the introduction of an artificial diffusion matrix (see also [14]).
The first scheme (method A) is based on the use of a primal-mixed formulation of the DD model written as a function of the quasi-Fermi potential gradient (see [12] and [13]).
Very recently, in 2010, Qin, et al. [12] introduced a iterative scheme method for finding a common element of, and common fixed point of infinite family of nonexpansive mappings.
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The efficient twin-tridiagonal (for calculating derivatives) and triple-tridiagonal (for solving partial difference equation with the CCD scheme) methods are also presented.
The genes were assigned to the MapMan hierarchical annotation scheme (Methods) providing high and low levels description for their functional role.
Table 1 Limiting behavior of various schemes Method Limiting ranking scheme Out-degree In-degree Right eigenvector Left eigenvector Kb α → 0+ α → 1/λ1− Kr α → 0+ α → 1/λ1−.
Despite the high costs of these schemes, methods are lagging to systematically evaluate their likely effectiveness amongst a geographical landscape of heterogeneous consumers.
We test our methods both against traditional HMMs and, on the SCOP dataset, on HMMs modified with entropy weighting schemes, methods that aim to extract the maximum from the training data in an information-theoretic sense.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com