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Notably, the two complexity settings show no difference on the ComParE set.
Results show that the classification performance with both feature sets is similar for arousal, while the ComParE set is superior for valence.
For the (larger) ComParE set, the linear kernel SVMs are superior, while for the smaller EmoFt set, the RBF kernel appears to be the better choice, which is expected due to the initial small feature space dimensionality.
Pitch- and jitter/shimmer-based features are found to be highly ranked in the proposed EmoFt feature set, while in the larger ComParE set, spectral band descriptors and MFCCs show an even higher correlation.
It can be clearly seen that for arousal classification, very few features are required and the results converge very quickly, while for valence classification, adding more features generally improves the performance, especially with the larger ComParE set.
While this discrepancy would be a limitation if the model were used to compare SET vs DET protocols, it would not affect the conclusions drawn on the use of gonadotropins.
Similar(53)
To compare sets, Cantor first distinguished between a specific set and the abstract notion of its size, or cardinality.
As Tynan ruefully wrote: "If I could laugh at the situation, I would compare setting up a film to making a jigsaw out of quicksilver".
We compare sets of roles by projecting roles from one set into the other set.
In order to compare sets, several preorders have been introduced (see e.g. [20 24]).
Therefore a study using the XPac approach, an algorithm developed to compare sets of complete crystal structures, has been conducted.
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