Sentence examples for matrices of training from inspiring English sources

The parameters for the prior are obtained from the MLLR transformation matrices of training speakers {W1,…,W S }: w ̄ ( i ) = 1 S ∑ s w s, ( i ) Σ ( i ) = 1 S − 1 ∑ s w s, ( i ) − w ̄ ( i ) T w s, ( i ) − w ̄ ( i ) (58).

R is the autocorrelation matrix of training samples.

where z l and ∑ l are the mean vector and covariance matrix of training samples, respectively.

Then, the sparse representation coefficients matrix of training sample sets for specific cloud type was used to form a projection subspace.

Following the standard machine learning paradigm, the feed-forward back-propagation ANN models were trained by feeding a matrix of training data complete with the input and target values.

For the nth subcarrier, we may, therefore, consider that we transmit a matrix of training signals, X n, where x n,q,m is the element in the qth row and mth column.

The memory requirements for wKSVD are much larger and correspond to the entire matrix of training signals, O(d N) or O(m N), since the iteratively weighted dictionary update repeatedly accesses residuals, coefficients and masks.

Let à ∈ R m × l be the normalized matrix of training samples transformed into the feature space and t ~ ∈ R m × 1 be the normalized transformed feature vector of the test image, where m is the dimensionality of the feature space and l the number of training samples.

The kernel matrix of all instances in D is denoted by K, the kernel matrix of training instances by KS and the kernel matrix of training versus test instances by KU.

Once a cut-off point was selected for a given variable, the resulting 2 × 2 classification matrix of training data enabled sensitivity and specificity to be computed [ 17].

randomly partition the sample set into five subsamples; retain one subsample as the validation set and aggregate the remaining k=4 subsamples into a matrix of training data, t ij (i∈{1,…, k}, j∈{1,…, p}) so that the intercept in the regression model will always be estimated by (Montgomery and Runger, 2007); centre and transform the training data into a new set of variables as; where,.