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(6) Select the testing samples of each class, and calculate the precision of classification result.
In this experiment, we divided training samples of each class into five portions of 20%%.
The number of cloud samples of each class varies from 140 to 350, and the detailed numbers are listed in Table 2.
On the other hand, LDA is based on the assumption that the samples of each class approximately create a Gaussian distribution.
It is generally good practice to use about 30×p samples of each class, where p is a multiple coefficient (Algorithm 1 step 3.a).
LDA can analyze clusters distributed in a global data space based on the assumption that the samples of each class approximately create a Gaussian distribution.
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The ground truth available is designated into sixteen classes with variable number of samples for each class (see Table 1).
A second version of the dataset includes four additional actions (drive car, eat, fight and run) and an increased number of samples for each class.
Since the objective was to create a classifier being able to discriminate between the different classes, we needed a balanced training dataset, or at least a large number of samples for each class.
The new set of samples (20% of the samples for each class) is again formed to have a new test set, and the remaining ones are the new training set.
The training set consisted of 20 samples for each class (each digit) with 5% of uniform random noise added to every sample fed into the SNN. Figure 5 Patterns for network training of 10 hand written digits (Semeion dataset).
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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