Sentence examples similar to third valued feature from inspiring English sources
In this model, we distinguish two kinds of textual values of a feature: First, the feature value is already in the form of a set of expressions, such as the value of features tags, category, sentiment, and emotion.
Even US-style family values feature.
Second, the feature value is in the form of a general text, such as the value of the feature content.
First, the feature value is already in the form of a set of expressions, such as the value of features tags, category, sentiment, and emotion.
From Equations 2 to 4, we can conclude that (R + M − 1) frame (0.39 s for fixed R = 30 and M = 10) information is needed to acquire the first LSFM feature value.
The first feature is value-hierarchical, Up-Down thinking that attributes greater value to that which is "Up" than to that which is "Down".
For each feature, a value with zero is deleted first, then feature selection is performed based on the statistical comparison.
The third feature, based on kernel density estimation (KDE), is the "KDE Peak" value.
We address the third feature using a formal description of a dialogue from which preferences over values emerge.
