Table 11 also presents the comparison of three different models (i.e., Word2Vec or Para2Vec, LDA and TF-IDF) in generating the vector representations of documents.
In addition to the above baseline, the topic model LDA [5], and TF-IDF [18] are compared to Word2Vec or Para2Vec in generating the vector representations of documents in our experiments.
Binary codewords c ∈ Z 2 N where Z 2 = { 0, 1 } are generated from the vector of information bits u ∈ Z 2 K using the generator matrix G ∈ Z 2 K × N, i.e., c=u G, and all binary operations are considered over a Galois field GF(2).
TN_curve Num 2.0: a generator for the 75-D vector We exploit a software package titled TN_curve Num 2.0 that can generate the 75D vector of a given DNA sequence, with which one can also generate the 75D vector of the corresponding shuffled sequence.
In order to generate the coefficient vector of Equation 38, we are interested in the inverse of the deterministic autocorrelation matrix V d (k).
Then, in the next step, an improved TF-IDF method is used to calculate the feature weights for each word to generate the feature vector of each text.
L'Ecuyer develops lower bounds for the maximum distance of the parallel hyperplanes generated by the vectors of nonsuccessive random numbers (RNs) obtained from multiple recursive generators (MRGs).
In other words, the desired signal belongs to the subspace generated by the vectors of G k =U k H k k V k, while the interference is completely eliminated.
The proposed method generates the flux vectors v as linear combinations of the basis vectors spanning the null space of S, and those satisfying the inequalities (4) and (5).
In conventional C-MFA, a metabolic model M is an algebraic equation used to generate MDV j sim from the vector of metabolic flux (v) and the isotopic labeling pattern of a carbon source (xinp), as shown in (1).
