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Hacking has provided us with the most detailed and careful philosophical approach to addressing issues in classification of mental disorder, and his work has been very influential.
However, accuracy of classification based on such reporting has not been validated,[ 9] and our results suggested potential issues in classification.
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The tool to deal with this issue in classification trees is to assign different costs to misclassifications, i.e., specifying that misclassifying a SN patient as a non-SN case is X time worse (or more costly) than the misclassifying a patient in opposite direction, where X is a user-specified parameter.
This paper discusses three different issues in feature classification.
Partan, S. & Marler, P. Issues in the classification of multimodal communication signals.
In this paper, we address the architectural design of the ELM classifier network, since too few/many hidden nodes employed would lead to underfitting/overfitting issues in pattern classification.
The debate concerns: a) current issues in the classification of eating disorders in the DSM-IV, b) changes proposed for DSM-V, c) neuroimaging eating disorder research and d) genetic eating disorder research.
Although morphology-based methods are instrumental tools for obtaining spatial information, the shape of the structuring element (SE) and computational complexity are still key issues in such classifications [18].
Structure determination based on supervised learning is an important issue in pattern classification.
An important issue in the classification of cognitive remediation techniques is the amount of strategy teaching involved [ 56].
One important issue in the classification of functional variants is the selection of an accurate negative set of non-functional ones.
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