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There are some slight differences between these classification systems.
Numerous methods exist to perform these classification tasks.
To date, there are no data from any comparative analysis of these classification systems.
Moreover, for correct estimation of parameters of these classification algorithms, a training corpus is needed.
These classification is based on both severity for the disease and image properties.
Usually another learning procedure is needed to combine these classification outputs.
(These classification scores are available at: http://www.ct.gov/doc/lib/doc/PDF/PDFReport/ClassificationManualLibraryCopy.pdf).pdf
Among these classification algorithms Voting Feature Interval has obtained outstanding performance in all the three datasets.
Then, we used these classification criteria to describe and compare fourteen existing environmental indicator sets.
These classification systems provide detailed stand-level characterization of forest ecosystems at a local level.
Computational experiments are conducted to evaluate the performances of these classification techniques.
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