Sentence examples for separation of features from inspiring English sources

In theory, the adaptive method should be well suited to the separation of features in all circumstances.

Firstly, we required a clear separation of features in the cortex, achievable by selecting the features with sufficient spatial distance between them.

For our application it is important to preserve size and separation of features such as nuclei, so we typically use the dead leaves approach [ 7], which simply replaces overlapping regions with the new frame.

Accordingly, the training sample data [F X (m) T, F Y (m) T ] T, m = 0,1,…, N train − 1 could be segmented into N ESN groups on the basis of the separation of feature space.

Although they achieve great improvement compared with the traditional hashing methods, the separation of feature and hash function learning cannot sufficiently exploit the power of deep neural network to learn effective binary code representation.

Linearly separable subcode (LSSC) [25] was put forward to tackle the aforementioned inabilities of DBR and BRGC effectively in fully preserving the separation of feature points in the index domain when the eventual distance evaluation is performed in the Hamming domain.

Because the fact that the difference between any pair of interval indices is not equal to the Hamming distance incurred between the corresponding DBR and BRGC codeword labels, the separation of feature components in the Hamming domain will eventually become poorer when more and more segmentations are applied to each single-dimensional feature space.

Hence, separation of feature value distributions between the samples from different states will be relatively clearer, and thus classification accuracy as a measure of feature set's discriminatory power can be biased.

However, statistical separation of data features between groups does not imply that those features have a predictive value in foretelling the group to which a novel example will belong.

Their analysis leads to a prediction that max pooling is most suitable for the separation of sparse features.

Actually, the calibration to Fermi energy level was not necessary as it is the relative energy separation of spectral features that is of importance for the ultimate results.