نتایج جستجو برای: labeling method
تعداد نتایج: 1675814 فیلتر نتایج به سال:
in this paper we define a new labeling called skolem odd difference mean labeling and investigate skolem odd difference meanness of some standard graphs. let g = (v,e) be a graph with p vertices and q edges. g is said be skolem odd difference mean if there exists a function f : v (g) → {0, 1, 2, 3, . . . , p + 3q − 3} satisfying f is 1−1 and the induced map f : e(g) → {1, 3, 5, . . . , 2q−1} d...
A novel relaxation labeling (RL) algorithm is proposed. RL is posed as a constrained optimization problem and the solution is found by using the Lagrangian multiplier method and a technique used in the graded Hoppeld neural network. In terms of the optimized objective value, the algorithm performs almost as well as simulated annealing, as shown by the experimental results. Also, the resulting a...
specified by a set of weighted preEerences, given by support functions si(R;p), which are functions of the labeling assignment 5. A separate function is given for each label at each object. The support si(R,F) can be positive or negative, and denotes the support which the current mix of weighted assignments lend to the prtiposition that object ai is label XX. In nearly all applications to date,...
Introduction: The field of stem cell biology and regenerative medicine is rapidly moving toward translation to clinical practice. Stem cell therapy seems to be a new treatment option for some diseases. So, tracking the distribution of stem cells is crucial to their therapeutic use. Based on this fact we labeled human mesenchymal stem cells (MSCs) with 111...
A novel relaxation labeling (RL) method is presented based on Augmented Lagrangian multipliers and the graded Hoppeld neural network (ALH). In this method, an RL problem is converted into a constrained optimization problem and solved by using the augmented Lagrangian and Hoppeld techniques. The ALH method yields results comparable to the best of the existing RL algorithms in terms of the optimi...
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