At each sampling step, we provided a criterion to determine the optimal sampling point, which maximizes the value of the product of curvature and square of minimum distance to other design sites.
For completeness, we present models that include all measured traits (Table 2), models without swimming speed (which maximizes our sample size; Table 3), and a model using the set of independent variables corresponding to the smallest AIC score (Table 4).
We also suggest exploration of sampling designs which maximize temporal overlap; for example, a systematically-staggered sampling with a predictable amount of overlap at different homes might allow for a simplified temporal correction term.
Given a set of training samples, the SVM training phase tries to find the optimal hyperplane, which maximizes the distance of the training samples nearest to it (called support vectors).
Based on structural risk minimization theory, SVMs generate an optimal classification hyperplane, which maximizes the margin between the hyperplane and the nearest samples of the classified sample categories and sets an equal margin.
In the classification stage, the test sample x will be classified to the class t ∈ {0,1}, which maximizes the conditional probability P(t|x, w).
A core-collection of 24 accessions, which maximizes the amount of genetic diversity in the sample [17] while including a limited number of individuals, was used for the whole set of experiments, minus Alc-0 (178and and Stw-0 (92AV).
These comparisons are based on quadruplicate testing, which maximizes detection of low HIV copies in a sample.
In addition, the benefit of our approach is that we provide data applicable to synovial samples from patients with different disease durations and different pharmacological treatments, which maximizes differences between patients [ 1], thus increasing the representativeness of our study.
which maximizes the criterion (13).
