Titlebook: Stochastic and Chaotic Oscillations; Yu. I. Neimark,P. S. Landa Book 1992 Springer Science+Business Media Dordrecht 1992 Generator.Mathema

气泡

书目名称Stochastic and Chaotic Oscillations影响因子(影响力)




书目名称Stochastic and Chaotic Oscillations影响因子(影响力)学科排名




书目名称Stochastic and Chaotic Oscillations网络公开度




书目名称Stochastic and Chaotic Oscillations网络公开度学科排名




书目名称Stochastic and Chaotic Oscillations被引频次




书目名称Stochastic and Chaotic Oscillations被引频次学科排名




书目名称Stochastic and Chaotic Oscillations年度引用




书目名称Stochastic and Chaotic Oscillations年度引用学科排名




书目名称Stochastic and Chaotic Oscillations读者反馈




书目名称Stochastic and Chaotic Oscillations读者反馈学科排名

个人长篇演说

Yu. I. Neimark,P. S. Landac corpus of the Kazakh language was collected for carrying out experiments and calculations. A study of various approaches and a hybrid approach for the semantic analysis of the Kazakh language was carried out. The practical part was implemented in Python. The article presents the results of experim

爱哭

Yu. I. Neimark,P. S. Landam models to improve the performance and user satisfaction of the recommendation system is still the main task of the recommendation system based on deep learning. This article reviews the research progress of recommendation systems based on deep learning in recent years and analyses the differences

摘要记录

Yu. I. Neimark,P. S. Landam models to improve the performance and user satisfaction of the recommendation system is still the main task of the recommendation system based on deep learning. This article reviews the research progress of recommendation systems based on deep learning in recent years and analyses the differences

吞下

附录

Urologist

协定

Yu. I. Neimark,P. S. Landae and false negative based confusion matrix table found revealed the robustness of the proposed models. General accuracy of self-supervised learning based the area under a ROC curve proposed with greater than 94% is also support an outstanding model studied. Therefore, rank of 1% to 10% of fine-tuni

ARC

Yu. I. Neimark,P. S. Landahods based on deep learning can achieve high accuracy they need data annotated by humans, which is time-consuming and costly. To overcome the above mentioned disadvantages this work proposes a hybrid topic modeling method that combines the advantages of both unsupervised and supervised methods. We b

矛盾

Yu. I. Neimark,P. S. Landane the best loss-based model. Based on the best loss-based model, the class-wise precision and sensitivity using a neighborhood size are also presented. The results show that the contrastive loss-based deep metric learning model achieved the highest precision of 94.90%, sensitivity of 94.85%, specif