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After converting the error to ppm, the errors are collected into 0.5 ppm bins.
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Time to complete the task, number of steps, and errors were collected.
Time to complete the task, the number of steps, and the number of errors were collected.
Then, a virtual spot forecast is performed on the test data set and the forecast error samples are collected.
Incrementally, the samples that do not conform to the given objective function (for example, their classification error is more than a threshold, or their reconstruction error is high) are collected and are used for adding new nodes to the hidden layer, with these new nodes being initialized based on those samples.
In this paper, we present a methodology for analyzing several quality characteristics simultaneously using the mean square error (MSE) criterion when data are collected from a combined array.
Sensitivity analyses demonstrate that the estimation of the λ values is dependent on the number of concentration measurements assimilated, the locations from which the measurement data are collected, the error assigned to the measurement values, and the correlation length of the λ fields.
Finally, the bit estimates are collected for bit error rate (BER) and block error rate (BLER) evaluations.
Due to the correlation of genotypes in two samples from the same population, very similar FST values can be expected unless there are differences in the populations or errors in the way the genotypes are collected.
In this paper we present a method to optimize multiple quality characteristics based on the mean square error (MSE) criterion when the data are collected from a combined array.
Once samples are collected, another potential source of error arises from the interpretation of histology.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com