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To answer this question, we base our method on the "Heuristical Theorizing" research approach [13].
We base our method on a stochastic nature of the messages as they evolve during FG/SPA processing.
In conjunction with a well-accepted learning algorithm, L1-regularized logistic regression (Friedman et al., 2010), we base our method on a large collection of causal relationships manually curated from the literature.
Similar(57)
Motivated by [10, 11], we base our methods on the construction of self-similar solutions and on the comparison arguments.
We base our methods on fixed-effects models.
We based our method on an established and dependable image-processing program developed at the National Institutes of Health (NIH) that is freely available.
Although we based our methods on those used in Altincicek et al.'s paper, our methods did differ in some respects, which may explain the differences in our results (Altincicek, personal communication).
For distance estimation, we base our localization method on the analysis of mode phases.
Using the random walk with restart algorithm on a heterogeneous network, we compared the performance of the heterogeneous network built based on our method with that based on the OMIM-based disease similarity network.
We repeat this comparison in order to compare the results based on our method with those based on GLM.
d Result based on our method, SNR = 13.88 dB.
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