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The suitable probabilistic thresholds having some level of error tolerance are evaluated in relevance to the data by using decision-theoretic rough set (DTRS) model, Bayesian rough set (BRS) model, variable precision rough set (VPRS) model, game-theoretic rough set (GTRS) model (Yao 2007; Śle et al. 2005; Ziarko 1993; Herbert and Yao 2011; Azam and Yao 2014).
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Performance measures are presented over increasing levels of error tolerance.
For both levels of error tolerance we identified the ten most sensitive and ten most specific models – giving a total of 40 models.
To cater for modules involving miRNAs that do not target all mRNAs in the module, and the presence of false positives and false negatives in the network, we have defined eight different types of MTB with different levels of autonomy and error tolerance.
In this case, the BP (1.3) is reformulated as min_{xinmathbb{R}^{n}}|x|_{1}quad mbox{subject to}quad|Ax-b |_{2}leq epsilon, (1.4) where (epsilon>0) is the tolerance level of errors and (|cdot|) is a norm on (mathbb{R}^{n}).
Figure 12 illustrates the result of the error tolerance range.
The computational reductions are a function of the error tolerance prescribed, with greater computational reductions as stricter tolerances are enforced.
where ϵ i ≥ 0 and E is the error tolerance level.
Typical castings for gun parts have an error tolerance of plus or minus five thousandths of an inch.
The effectiveness of models was further evaluated using margin of error and tolerance interval.
Every numerical calculation was computed with a relative error tolerance of 10−6 and an absolute error tolerance of 10−9.
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