Titlebook: Learning and Intelligent Optimization; 13th International C Nikolaos F. Matsatsinis,Yannis Marinakis,Panos Par Conference proceedings 2020

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書目名稱Learning and Intelligent Optimization影響因子(影響力)




書目名稱Learning and Intelligent Optimization影響因子(影響力)學科排名




書目名稱Learning and Intelligent Optimization網(wǎng)絡公開度




書目名稱Learning and Intelligent Optimization網(wǎng)絡公開度學科排名




書目名稱Learning and Intelligent Optimization被引頻次




書目名稱Learning and Intelligent Optimization被引頻次學科排名




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書目名稱Learning and Intelligent Optimization年度引用學科排名




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書目名稱Learning and Intelligent Optimization讀者反饋學科排名

大炮

羽飾

Towards Improving Merging Heuristics for Binary Decision Diagrams, multigraphs and represent the solution space of binary optimization problems in a recursive way. During their construction, merging of nodes in this multigraph is applied to keep the size within polynomial bounds resulting in a discrete relaxation of the original problem. The longest path length th

物種起源

On Polynomial Solvability of One Quadratic Euclidean Clustering Problem on a Line,racluster sums of the squared distances between clusters elements and their centers. The centers of some clusters are given as an input, while the other centers are unknown and defined as centroids (geometrical centers). It is known that the general case of the problem is strongly NP-hard. We show t

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DEFT

A Hessian Free Neural Networks Training Algorithm with Curvature Scaled Adaptive Momentum, (HF-CSAM). The algorithm’s weight update rule is similar to SGD with momentum but with two main differences arising from the formulation of the training task as a constrained optimization problem: (i) the momentum term is scaled with curvature information (in the form of the Hessian); (ii) the coef

流眼淚

Irreducible Bin Packing: Complexity, Solvability and Application to the Routing Open Shop,f any two bins is larger than the bin capacity. There is a trivial upper bound on the optimum in terms of the total size of the items. We refer to the decision version of this problem with the number of bins equal to the trivial upper bound as Irreducible Bin Packing. We prove that this problem is N

存在主義

Learning Probabilistic Constraints for Surgery Scheduling Using a Support Vector Machine,od for tackling probabilistic constraints using machine learning. The technique is inspired by models that use slacks in capacity planning. Essentially support vector classification is used to learn a linear constraint that will replace the probabilistic constraint. The data used to learn this const