Sentence examples for degrees of feature from inspiring English sources

The recognition accuracy rates of clean-train tasks averaged over 0-20 dB noisy test data with different degrees of feature post-processing are listed in Table 2.

Kohavi and John used the above theoretical representations to claim that two degrees of feature relevance (strong and weak) are required in terms of an optimal classifier.

We use a sharpness factor in the weighting function to control the degree of feature preserving.

Since we make adjustments according to the sparsity degree of feature maps in the adaptive pooling algorithm, the experimental results further confirm that whether a pooling method is suitable depends on the sparsity degree of features activations.

Various interpretations of the meaning of 'entropy' or 'information content' are possible, including treatment as a chemical diversity measure or the degree of feature conservation.

Now that most of Facebook products, including Instagram Stories, Facebook Stories and Messenger Day, have their own augmented reality face filters, they've hit a degree of feature parity with Snapchat.

Neurons with similar FSIs are more likely to be functionally connected, but the degree of feature selectivity is subject to substantial modification by target neuron processing.

First of all, for each feature f i,SU i,c (see Eq. 6) is calculated for estimating the degree of feature relevance (line A1 1).

Moreover, Lee et al. (2006) neither explicitly manipulated the degree of feature overlap in their visual stimuli nor did they relate performance specifically to perirhinal cortex integrity.

Feature class relevance: It is defined as the degree of feature class mutual information between a feature f and a class label C. Feature-feature relevance: It is defined as the degree of feature-feature mutual information between any two features, say (f_i) and (f_j) where (f_i, f_j in F).

Customers' decision making process is modeled with an adaptive fuzzy inference system in which the input variables are the degrees of price, feature, and greenness and output variables are the estimated rating data.