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Figure 7 shows the results of the classification algorithm for adjacent frames.
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Table 2 reports the results of the classification algorithms for both a training set and a cross-validation set.
Even if the proposed object representation serves for describing a large variety of objects, the result from the classification algorithm is a coarse description of the object.
They discourage from optimizing the choice of the classification algorithm based on the obtained results.
Performance of the classification algorithm was validated previously using a leave-one-out procedure and resulted in similarity indexes of at least 0.808, indicating excellent agreement [ 20].
The CMDs represent the first step of the classification algorithm.
The results of varying several of the parameters of the classification algorithms are shown in Table 1.
This modification resulted in the disappearance of any significant effect of extrapolation degree on MCC for each of the classification algorithms (Figure 3).
It seems that the accuracy of results obtained does not depend on the classification algorithm, since the best models chosen using the statistical test are built using all the different supervised classification algorithms tested.
While interpreting the results of Table 5, we should note that the classification algorithm did not take advantage from increasing the number of features.
These results remain consistent, regardless of the scale used for the classification algorithm.
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