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For both SVMs we set the tolerance for stopping criteria to 0.001 and the penalty parameter (C=1).
Edit boxes in the "Deconvolution" panel are used to set the "Tolerance for ABIC optimization", as well as the initial values for ln u) (i.e., smoothness), position shift, and length correction.
In contrast, if we set the tolerance level of the other two methods to a low value to boost the low noise performance, their performance would be much worse at high noise level, as shown in Figure 5c (for example the performance of the sinc basis-blue curve-is 7.7 dB worse than in Figure 5a when input ENOB is 6).
For ARACNE we set the tolerance parameter for the DPI to 0.1, as suggested in [ 24].
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It's best to do some research when setting the tolerance band.
This parameter sets the tolerance of the activation function to large input values, the larger this value is the less sensitive the activation function becomes.
After setting the tolerance and initial values, users can start searching for optimized deconvolution by left clicking on the push button "Perform Deconvolution" in the "Deconvolution" panel.
For setting the tolerance level ftol no straightforward method exists.
The value assigned to the second parameter sets the tolerance within which edges are included in the Δ‐ultrametric network.
The times required at a mismatch tolerance of ≤3 are comparable to those reported for other recently developed tools (Li et al., 2008), and it should be noted that although setting the tolerance above three results in a significantly increased run time, the amount of usable sequence data increases dramatically as well.
The tol input must be an array (NumPy array or Python list) of length n (the dimension specified in the nlopt.opt constructor); alternatively, you can pass a single number in order to set the same tolerance for all optimization parameters.
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