The phrase "any two features" is correct and usable in written English.
It can be used when discussing or comparing two specific characteristics or attributes of something.
Example: "You can choose any two features from the list to customize your product."
Alternatives: "any pair of features" or "two selected features".
The matching scores encoding the similarities between any two features are then refined using link PageRank and structure similarity to further enhance distinctive features while suppress noisy ones.
For example, it may provide users with the feature types important to activity/identity, the correlation between features with high identity information, or the similarity between any two features.
Combining the MODGDF and the spectral magnitude-based features gives a significant increase in recognition performance of 11% at best, while combining any two features derived from the spectral magnitude does not give any significant improvement.
Again, for a given selected feature (f_k), EFS-MI considers any two features say (f_i) and (f_j) as redundant, if (feature_feature_MI(f_i,f_k)=feature_feature_MI(f_j,f_k)) and exclude from further consideration.
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).
Given the set of importances and interactions defined above, we can easily verify that the outcome of the Choquet aggregation will exceed this threshold for combinations of any two features involving similarities for the phone number and at least one other feature (besides the date).
The distance between any two feature vectors v1 and v2 in the DTF space is computed as: d DTF = ∑ i = 1 m ( v 1 i - v 2 i ) 2. (7).
This equation means that for any two feature subspaces, their coselection rate is, in theory, about D/ K.
But there are also a number of theories of desire that refuse to privilege any one feature.
In order to reduce the computations, each feature map in layer 1 was connected to any nine feature maps in layer 2. In other words, 36 feature maps in layer 2 were connected to a pair of maps in layer 1 and remaining 9 were singly connected.
This is to say that if any of the two features from the unknown sample meets the criteria (Rfeature_i) to be either normal/tumor tissue type (based on our definition, Rfeature_i is a region with 100% discriminating efficiency for a specific tissue type), the unknown sample will be assigned to be normal/tumor respectively.
