The decision function f (x ′ ) = s i g n (∑ i = 1 p α i k (y i y ′ ) + b is used to determine the class type of a testing sample x ′, where y ′ is its corresponding meta-sample computed from DCA.
A set of 13 attributes per image sample were computed from color and texture models using HSI statistics and gray level co-occurrence matrices (GLCMs) probabilities.
In order to approximate the expectations by sample averages computed from training data, the side-information variable Y is discretized to Ŷ ∈ η 1, … η Y, where is a large enough positive integer.
The inputs are either raw data or sample moments computed from the data, and a model to be evaluated.
Thus, the use of the t test, the χ test, and the analysis of variance F test depends on the appropriateness of the normal distribution as an approximation for the distribution of sample means computed from the data.
As a final result, when the simulated distribution in 10 kg samples was computed from the prior simulation, it was very similar to the distribution observed in representative samples for the same sample size from the RASFF.
The hydrogen atom or electron donation capacity of the corresponding samples was computed from the bleaching property of the purple-colored methanol solution of 2,2-diphenyl-1-picrylhydrazyl (DPPH).
A median expression profile of progressive samples was computed from these differential genes.
Genetic distances (d) of samples were computed from Nei-Li's coefficient of similarity, as d = 1 - s[ 37].
Gene expression levels in wild-type, RNAi, and mutant samples were computed from Affymetrix.CEL files of raw gene expression data using Robust Multi-Array Average [ 51] with the Bioconductor "affy" package [ 52].
The parameters and are the sequence-level sample covariance coefficient obtained from the test sequence S and the global sample covariance coefficient computed from the whole training features, respectively.
