Finally, Ross also points out that Binmore's objectives are as much normative as descriptive: he aims to show egalitarians how to diagnose the errors in conservative rationalisations of the status quo without calling for revolutions that put equilibrium path stability (and, therefore, social welfare) at risk.
Here again, Kant diagnoses the error or fallacy contained in this syllogism as that of ambiguous middle.
A nice illustration of its use is found in Xunzi's essay, "Undoing Fixation," in which Xunzi diagnoses the errors of other philosophers, each of whom focuses on some one aspect of the Way (an aspect that is legitimate in itself) to the exclusion of others (see Xunzi 21; Hutton 2014, 224 35).
Furthermore, we will diagnose the residual error model, for which an important diagnostic plot is that of absolute individual weighted residuals (|IWRES|) vs. individual predictions (IPRED).
The expert practitioner of this art is the Epicurean sage, the community's leader, who must look at the errors in the behavior of his companions, including other sages and even himself, diagnose the moral errors and circumstances which led to that behavior, and know what therapy to apply.
The role of interactive mediations is to diagnose the sources of error and to help the learner to overcome it, rather than to simply document when an error has occurred.
If errors are found, we seek to diagnose the source of the error.
The World Health Organization has recommended the widespread deployment of a new molecular machine — which can diagnose the actual disease, without error, in a matter or hours.
The World Health Organization has recommended the widespread deployment of a new molecular machine which can diagnose the actual disease, without error, in a matter or hours.
Error recovery systems sometimes fail to accurately diagnose an error because the parser has performed reductions based on erroneous input.
The real success, he says, will be the pairing of human expertise -- and the comfort that patients need when dealing with difficult diagnoses -- with machine learning that, driven by algorithms, can reduce the error rate of cancer diagnoses, for example, to about 0.5%.
