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The success of such methods is typically evaluated by how well they predict human judgments of similarity.
The reliability of newly introduced measurement methods is typically tested against the predictions of the theory rather than the other way around.
The uncertainty associated with these methods is typically high, since parameters based on geometrical averages can fail to describe important characteristics of surface topography.
Since it has a fluorinated alky-chain configuration and is non-biodegradable, its treatment by conventional oxidation methods is typically very inefficient.
Since practical implementation of grey-box extremum seeking methods is typically done using digital technology, this paper takes advantage of emulation design methods to port the existing continuous-time grey-box extremum seeking methods to discrete-time frameworks.
Performance of machine learning methods is typically evaluated using predictive accuracy.
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AAC methods are typically differentiated as aided or unaided.
CASA methods are typically unsupervised.
Filter methods are typically much less computationally intensive than wrapper methods [40].
Filter-based feature selection methods are typically faster but the classifier accuracy is not ensured.
To prevent such behavior, more robust statistical methods are typically employed.
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