Sentence examples for norm distance from inspiring English sources
We generalize this by counting the pixels for which the Minkowski infinity norm distance to the center of the hyper-cube is smaller than.
Its main purpose is to design the digital fuzzy controller achieving the minimum norm distance between the trajectories of the approximate closed-loop systems under the analog and the digital controls.
It is verified that, if there exist the digital gains such that the approximate norm distance is sufficiently small, the redesigned controller makes the trajectories of the exact closed-loop systems close, or, at least, asymptotically stabilizes the plant.
All steps for this method is same as the goal programming method, but in goal attainment method the distance between objective functions and ideal point (target point) is calculated in infinity norm distance.
Furthermore, ∥·∥ denotes the L - 2 norm distance or Euclidean distance, H denotes the Hermitian transpose of a matrix, and I and Q respectively denote the in-phase(IP) and quadrature-phase (QP) parts of a signal.
The proof establishes the equivalence of the cb-norm distance and the Bures distance for completely positive maps.
Inspired by the advantages of least squares twin support vector machine (LSTWSVM), TBSVM and L1-norm distance, we propose a LSTBSVM based on L1-norm distance metric for binary classification, termed as L1-LSTBSVM, which is specially designed for suppressing the negative effect of outliers and improving computational efficiency in large datasets.
In goal programming approach, we use 1-norm distance (city-block distance) to evaluate distance between objective function and ideal point.
Furthermore, the distance in least squares TBSVM (LSTBSVM) is measured by L2-norm, but L1-norm distance is usually regarded as an alternative to L2-norm to improve model robustness in the presence of outliers.
Toward the end, we design a new l1-norm distance metric for measuring misclassification errors and use it to develop a mixed 0 1 integer and linear program (MILP) for the k piecewise linear and convex separation of data.
Starting from an initial efficient solution, Sylva and Crema's (2007) algorithm finds at each iteration a new one that maximizes the infinity-norm distance from the set dominated by the previously found solutions.
