Sentence examples for matrices of classes from inspiring English sources

In [6], Mohiuddine and Aiyup defined the space ω ( p, s ) and obtained necessary and sufficient conditions to characterize the matrices of classes ( ω ( p, s ), V σ ), ( ω p ( s ), V σ ) and ( ω p ( s ), V σ ) reg.

where is the mean and the sample size of class, ; is the maximum likelihood estimates of the covariances matrices of classes ; represents the new space that maximizes the criterion.

In this paper we characterize the matrix classes from this space to the space V σ λ, i.e., we obtain necessary and sufficient conditions to characterize the matrices of classes ( ω ( p, s ), V σ λ ), ( ω p ( s ), V σ λ ) and ( ω p ( s ), V σ λ ) reg.

The maximum likelihood classifier (threshold level of 0.95, i.e. 95%) was also adopted, a widely used classification procedure of digital remotely sensed data (Foody et al. 1995), and it takes the variability of classes into account by using the covariance matrices of classes (Schriever and Congalton 1995).

All these new synthetic matrices of class I was comparable to existing MALDI matrix, HCCA in terms of intensity for the detection of peptides.

We assembled a matrix of classes (niches or phenotypes) containing the niche metadata for all 274 isolates (Additional file 1), including sampling date (year, time), sampling location (continent, space), and source (clinical or environmental, space).

Our study allows to define risks of transmission between classes of individuals by calculating matrices of contacts of class-class type along the three quantities describing a contact event that we considered – the number of contacts, the number of distinct contacts, the overall duration of contacts.

where (boldsymbol {p}_{C}^{ k)}) and V (k) are the centroid and the covariance matrix of class k, respectively.

where μ i is the mean vector and C i is the covariance matrix of class i=1,2.

The vector x ¯ i is the unbiased sample mean and the matrix S i is the sample covariance matrix of class ∏ i.

Again this is the matrix of class means, whose element, μ i, k, represents the mean of gene i across samples belonging to class k.