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In these years, signature expansion increased the accuracy of the classifications, relative to the best single-image classification, with 11% (2005) and 17% (2006) (Table 2).
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Also, the accuracy of the classification is 90%%.
Post-classification refinement was used to improve the accuracy of the classification.
Accuracy of the classification ranges from 87 to 92 % indicating consistent classification results.
The accuracy of the classification was assessed by comparison with manual classification.
In general the selection of feature vectors greatly affects the accuracy of the classification.
Whether the statistical model is correctly established or not determines the accuracy of the classification method.
An error matrix was established to evaluate the accuracy of the classification.
If malicious classes are detected, decisions are done depending on the accuracy of the classification.
The effectiveness of the models is evaluated by comparing the performance and accuracy of the classification.
To improve and maximize the accuracy of the classification and also minimize the wrong classification, CSCA came to the picture.
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CEO of Professional Science Editing for Scientists @ prosciediting.com