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

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書目名稱Machine Learning, Optimization, and Data Science影響因子(影響力)




書目名稱Machine Learning, Optimization, and Data Science影響因子(影響力)學(xué)科排名




書目名稱Machine Learning, Optimization, and Data Science網(wǎng)絡(luò)公開度




書目名稱Machine Learning, Optimization, and Data Science網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning, Optimization, and Data Science被引頻次




書目名稱Machine Learning, Optimization, and Data Science被引頻次學(xué)科排名




書目名稱Machine Learning, Optimization, and Data Science年度引用




書目名稱Machine Learning, Optimization, and Data Science年度引用學(xué)科排名




書目名稱Machine Learning, Optimization, and Data Science讀者反饋




書目名稱Machine Learning, Optimization, and Data Science讀者反饋學(xué)科排名

Canvas

來就得意

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

懲罰

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.

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聯(lián)想記憶

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.