Titlebook: Machine Learning, Optimization, and Data Science; 5th International Co Giuseppe Nicosia,Panos Pardalos,Vincenzo Sciacca Conference proceedi

皱纹

书目名称Machine Learning, Optimization, and Data Science影响因子(影响力)




书目名称Machine Learning, Optimization, and Data Science影响因子(影响力)学科排名




书目名称Machine Learning, Optimization, and Data Science网络公开度




书目名称Machine Learning, Optimization, and Data Science网络公开度学科排名




书目名称Machine Learning, Optimization, and Data Science被引频次




书目名称Machine Learning, Optimization, and Data Science被引频次学科排名




书目名称Machine Learning, Optimization, and Data Science年度引用




书目名称Machine Learning, Optimization, and Data Science年度引用学科排名




书目名称Machine Learning, Optimization, and Data Science读者反馈




书目名称Machine Learning, Optimization, and Data Science读者反馈学科排名

Canvas

来就得意

Quantitative and Ontology-Based Comparison of Explanations for Image Classification,ions, and in particular the semantic component is systematically overlooked. In this paper we introduce quantitative and ontology-based techniques and metrics in order to enrich and compare different explanations and XAI algorithms.

deceive

惩罚

An Adaptive Parameter Free Particle Swarm Optimization Algorithm for the Permutation Flowshop Schede parameters are optimized together and simultaneously with the optimization of the objective function of the problem. This approach is used for the solution of the Permutation Flowshop Scheduling Problem. The algorithm is tested in 120 benchmark instances and is compared with a number of algorithms from the literature.

节约

联想记忆

GLOSS

Catheter

A Beam Search for the Longest Common Subsequence Problem Guided by a Novel Approximate Expected Lence. Results show in particular that our novel heuristic guidance leads frequently to significantly better solutions. New best solutions are obtained for a wide range of the existing benchmark instances.

otic-capsule

Relationship Estimation Metrics for Binary SoC Data,mated relationships to give accuracy scores. The metrics . and . based on covariance and independence are demonstrated to be the most useful, whereas metrics based on the Hamming distance and geometric approaches are shown to be less useful for detecting the presence of relationships between SoC data.