Sentence examples for between each data vector from inspiring English sources

In the Eq. (1), the measurement of non-similarity is expressed by the sum of squares of the distances between each data vector and the center of the corresponding class.

The main assumption of this work was to ignore the correlation between the data vector and the weights and compare the correlation between data vectors, preserving past and present data vector correlations.

For the k th user and the q th cluster at n th frame, the relation between the data vector S n ( S q ) and the received vector can be written as: Y n ( S q ) = H n ( S q ) X n ( S q ) + N n ( S q ) = H n ( S q ) W n ( S q ) P q S n ( S q ) + N n ( S q ) (7).

The second subspace algorithm optimizes the mutual information between the future data vector of observations and the innovation state vector.

The proposed transmitter represents a nonlinear one-to-one mapping between the transmitted data vector and the symbol vector.

Regarding the complexity of the proposed algorithm, an incremental update is performed to adjust the distance d(_{1j}) in the distance matrix with respect to the best matching between a current data vector X(_{j}) and its corresponding reference vector C(_{1}) as specified in (4) at step 2.1.2 of the cluster update phase.

We did not apply a linear transformation (operator) between data vector spaces in order to preserve the operation of data vector addition in the original data.

We can fit a linear regression between data vector Y and an explanatory variable X using this variance-covariance matrix and the multivariate normal distribution: (1) Y | X ∼ N (Xβ, Σ ) where β is a vector of regression coefficients.

Because the overall photon count in contraction data was about 2-fold greater than in rigor data we did not have to apply a linear transformation (operator) between data vector spaces in order to preserve the operation of data vector addition in the original data.

Only the (N−1 th part of each concatenated data vector was independent (one out of N−1 trials).

This validity index is highly efficient in determining clusters with the least similarity between them and the highest similarity between data vectors in each cluster.