Sentence examples for vectors of the training from inspiring English sources

SVM is a discriminative classifier, attempting to generate an optimal decision plane between feature vectors of the training classes.

For comparison of the proposed RBM-based feature extraction with PCA, the 50 most significant eigen vectors of the training data set after PCA were used as the features training and testing data.

The Q-stack vectors of the training sets are then fed into each of the GMM, LLR and RankSVM methods to create quality-dependent versions, which are called GMM (Q-stack), LLR (Q-stack) and RankSVM (Q-stack), respectively.

Accordingly, if M train and M test are the respective mean vectors of the training and test corpus, then each frame of the test corpus is multiplied by M train/M test.

During the training process, the genetic algorithm identifies a hyperplane which has a minimum distance from the vectors of the training set.

Finally, the codebook vectors of the trained SOM have been clustered automatically using Extended Fuzzy C-Means (EFCM).

In the combined prediction model comprising all 11 proteins, dimensionality reduction was applied to reduce the number of feature vectors for the training of machine learning classifiers.

Then, the difference between each LSF vector of the training corpus and its associated codeword is calculated.

The analysis is performed first by computing two covariance matrices: within-class scatter matrix S W = ∑ i = 1 K ∑ u k ∈ K i ( u k - μ i ) ( u k - μ i ) T, and between-class scatter matrix S B = ∑ i = 1 K ( μ i - μ ) ( μ i - μ ) T, where μ is a mean vector of the training set and μ i is a mean vector of the i th class (termed K i ).

The feature vector of the training set was normalized to zero mean and unit variance for every dimension independently and the normalization vector stored to normalize subtrials of the test set.

Composition of amino acids and amino acid physicochemical properties were extracted from protein sequences as feature vectors for the training of machine learning algorithms.