Titlebook: Artificial Intelligence and Machine Learning; 31st Benelux AI Conf Conference proceedings 2020

輕佻

書目名稱Artificial Intelligence and Machine Learning影響因子(影響力)




書目名稱Artificial Intelligence and Machine Learning影響因子(影響力)學(xué)科排名




書目名稱Artificial Intelligence and Machine Learning網(wǎng)絡(luò)公開度




書目名稱Artificial Intelligence and Machine Learning網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Intelligence and Machine Learning被引頻次




書目名稱Artificial Intelligence and Machine Learning被引頻次學(xué)科排名




書目名稱Artificial Intelligence and Machine Learning年度引用




書目名稱Artificial Intelligence and Machine Learning年度引用學(xué)科排名




書目名稱Artificial Intelligence and Machine Learning讀者反饋




書目名稱Artificial Intelligence and Machine Learning讀者反饋學(xué)科排名

北極熊

遭遇

onlooker

Extended Bayesian Personalized Ranking Based on Consumption Behaviorxtended form of BPR with consumption information improves the performance based on four evaluation measures. The result also verifies that by considering a more granular feedback the extended BPR has better predictions.

發(fā)微光

Dividing the Light from the Darknessaheuristic—simulated annealing—can be used as the optimization algorithm in the automatic modular design of robot swarms. The results indicate that simulated annealing is indeed a viable candidate. Additionally, we investigate the influence of some obvious variations of simulated annealing on the performance of the automatic modular design.

Grating

AutoMoDe-,: Automatic Modular Design of Control Software for Robot Swarms Using Simulated Annealingaheuristic—simulated annealing—can be used as the optimization algorithm in the automatic modular design of robot swarms. The results indicate that simulated annealing is indeed a viable candidate. Additionally, we investigate the influence of some obvious variations of simulated annealing on the performance of the automatic modular design.

Blood-Vessels

Towards Deterministic Diverse Subset Samplinghod to yield low-rank approximations of kernel matrices is evaluated by comparing the accuracy of the Nystr?m approximation on multiple datasets. Afterwards, we demonstrate the usefulness of the model on an image search task.

他日關(guān)稅重重

harrow

https://doi.org/10.1007/978-1-349-18048-6ric that can be incorporated into the augmented method of . nearest neighbors for ordinal classification. The experimental results show that our method is competitive with other modified machine learning methods and considering additional relative information leads to a better performance.

Immunotherapy