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In Reference [40], the authors have described two approaches for combining the large feature spaces to efficient numbers using genetic algorithm and fuzzy clustering techniques.
In addition, cortical algorithms (CA) are adapted to reduce the large feature set obtained as well as to efficiently identify composers in a supervised manner.
The large feature size, and the inherent 'stickiness' of the epicuticular lipids that are present on the surface of the wing make it quite difficult to produce accurate AFM scans that are free from artifacts.
In this sense, to circumvent the problems caused by the large feature vectors calculated from raw (unprocessed) hyper-spectral data when used for classification purposes, feature reduction (or decorrelation) techniques are usually applied to avoid the Hughes phenomenon [12 14].
However, the disadvantage might be that: some redundant features or noise data may exist among the large feature space.
Similar(55)
"And we would be able to see not only the small features that have strong signals, but the large features that have weak signals.
And the evidence concerning the large features of historical change over the past three millennia offers little support for the idea of one fixed process of civilizational development.
The large features are possibly a result of nucleation sites created by the multilayer GO nanoparticles.
In some cases, the largest feature might be less descriptive or less relevant than another feature.
Furthermore, identifying the largest feature is a technique that is easily measurable and completely objective.
The larger feature is likely to be the bigger-than-expected impact site of the lander.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com