Sentence examples for minimization section from inspiring English sources

As shown in Empirical risk minimization section, ( frac{l}{h} ) determines whether to use the empirical or the structural risk minimizations.

In the minimization section, we employed steepest descents [ 29] minimization with a maximum of 5,000 steps to remove bad van der Waals contacts.

In the minimization section, the steepest descent [ 52] minimization was employed with a maximum of 5,000 steps to remove bad van der Waals contacts.

According to [28] there are four steps to implement the structural risk minimization (see section Structural risk minimization), of which the first step is to choose a class of functions with hierarchy of nested subsets in ordered of the complexity.

For the purpose of minimization in Section 4, a hybrid approach combining the differential evolution algorithm with the Levenberg-Marquardt is proposed for which the combination algorithm and closed form equations for calculation of the Jacobian are provided.

From the two sets of 3D space points corresponding to the left and the right cameras, we then estimate the radius of the sphere using least square minimization techniques (Section 5.2).

From the two sets of 3D space points, corresponding to the left and right images, two values for the radius of the sphere are computed based on a least square minimization approach (Section 5.2).

For the Enumeration and Minimization step (Section 3), the steric filter prunes entire subtrees of the conformation search tree (cf. Lilien et al., 2005).

Below we expand on this approach to illustrate the general minimization problem (Section 2.1) and show how both penalty and LM formulations may be thought of equivalently as weakly penalized constraints in the continuous setting (Section 2.1.1).

Section 3 describes the high-frequency minimization algorithm and Section 4 describes how such compressed data are recovered through a concurrent binary search algorithm.

We also develop an efficient Iteratively Reweighted Least-Squares (IRLS) algorithm to recover block-sparse signals from given fewer measurements, which generalizes the algorithm of [28] to the unconstrained l 2/l q (0 < q ≤ 1) norm <span class="lh">minimization case in Section 6.