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That algorithm works by sequentially applying four simple classifiers to a document: first, a classifier to distinguish color from neutral documents; second, a classifier to distinguish text from non-text documents; another classifier to distinguish mix documents from photos/pictures; and a fourth classifier to decide between photos, pictures, and the mix class, as shown in Fig. 1 a.
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Therefore, we train two classifiers where the first classifier uses PCA + DIVS and classifies a test image into cars and vans.
The test images that were classified as cars are further classified into sedans and taxis using the second classifier which employs PCA + DFVS.
The first classifier uses the short-time Fourier transform to characterize the patterns, while the second uses the chirplet transform.
The outputs of these two classifiers will be combined and input to the third classifier, so that the final results can be obtained.
The second classifier is devised to ultimately address the fault diagnosis (FD) problem via the proposed methods in a multiple classifier (MC) ensemble.
This hybrid architecture is based on the idea that the correct class almost systematically belongs to the two more relevant classes found by the first classifier.
In Part II of the series, a second classifier named the area error method is devised for NPP supervision to ultimately address the FD problem using a multiple classifier system.
Our first classifier is a standard logistic regression classifier trained on tweets.
For example, a photo document misclassified as text by the second classifier will not be processed by the remaining classifiers.
Where; Dim is the Euclidean distance between class Ci from the reference classifier and class Pm from the second classifier.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com