תמונות העטיפה

Mean Field Guided Machine Learning

This book explores the integration of Mean Field Game (MFG) theory with machine learning (ML), presenting both theoretical foundations and practical applications. Drawing from extensive research, it provides insights into how MFG can improve various ML techniques, including supervised learning, rein...

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שמור ב:
מידע ביבליוגרפי
Main Authors: Kang, Yuhan (Author), Gao, Hao (Author), Han, Zhu (Author)
מחבר תאגידי: SpringerLink (Online service)
פורמט: אלקטרוני ספר אלקטרוני
שפה:אנגלית
יצא לאור: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
מהדורה:1st ed. 2025.
סדרה:Wireless Networks,
נושאים:
Machine learning.
Telecommunication.
Artificial intelligence.
Machine Learning.
Communications Engineering, Networks.
Artificial Intelligence.
גישה מקוונת:https://doi.org/10.1007/978-3-031-91859-9
תגים: הוספת תג
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תוכן הענינים:
  • Preface
  • Chapter 1 Overview of Mean Field Theory and Machine Learning
  • Chapter 2 Mean Field Game and Machine Learning Basis
  • Chapter 3 Opinion Evolution in Social Networks: Use Generative Adversarial Networks to Solve Mean Field Game
  • Chapter 4 Data Augmentation using Mean Field Games
  • Chapter 5 Mean Field Game Guided Deep Reinforcement Learning
  • Chapter 6 Incentive Mechanism Design in Satellite-Based Federated Learning using Mean Field Evolutionary Approach
  • Chapter 7 Client Selection in Hierarchical Federated Learning with Mean Field Game
  • Chapter 8 Evolutionary Neural Architecture Search with Mean Field Game Selection Mechanism
  • References
  • Index.

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