01112cam0-2200301 --450 991100199180332120250519140807.0978-88-498-8006-920250519d2024----kmuy0itay5050 baitaITy 001yyAppunti e idee per un liberalismo del futuro, ovvero Riflessioni a margine di due ritrovate lettere di Giovanni MalagodiVittorio Lorenzo TumeoSoveria MannelliRubbettino©202480 p.ill.21 cmUniversitàRiflessioni a margine di due ritrovate lettere di Giovanni MalagodiLiberalismoConcezione [di] Malagodi, Giovanni320.5109223itaTumeo,Vittorio Lorenzo1819635Malagodi,GiovanniITUNINAREICATUNIMARCBK9911001991803321SOC 873842/2025FSPBCFSPBCAppunti e idee per un liberalismo del futuro, ovvero Riflessioni a margine di due ritrovate lettere di Giovanni Malagodi4380073UNINA03950nam 22006015 450 991101568490332120250712072154.03-031-91859-210.1007/978-3-031-91859-9(MiAaPQ)EBC32202187(Au-PeEL)EBL32202187(CKB)39620867300041(OCoLC)1527722694(DE-He213)978-3-031-91859-9(EXLCZ)993962086730004120250709d2025 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMean Field Guided Machine Learning /by Yuhan Kang, Hao Gao, Zhu Han1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (248 pages)Wireless Networks,2366-14453-031-91858-4 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.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, reinforcement learning, and federated learning. MFG theory and ML are converging to address critical challenges in high-dimensional spaces and multi-agent systems. While ML has transformed industries by leveraging vast data and computational power, scalability and robustness remain key concerns. MFG theory, which models large populations of interacting agents, offers a mathematical framework to simplify and optimize complex systems, enhancing ML’s efficiency and applicability. By bridging these two fields, this book aims to drive innovation in scalable and robust machine learning. The integration of MFG with ML not only expands research possibilities but also paves the way for more adaptive and intelligent systems. Through this work, the authors hope to inspire further exploration and development in this promising interdisciplinary domain. With case studies and real-world examples, this book serves as a guide for researchers and students in communications and networks seeking to harness MFG’s potential in advancing ML. Industry managers, practitioners and government research workers in the fields of communications and networks will find this book a valuable resource as well.Wireless Networks,2366-1445Machine learningTelecommunicationArtificial intelligenceMachine LearningCommunications Engineering, NetworksArtificial IntelligenceMachine learning.Telecommunication.Artificial intelligence.Machine Learning.Communications Engineering, Networks.Artificial Intelligence.006.31Kang Yuhan1833765Gao Hao1064416Han Zhu732360MiAaPQMiAaPQMiAaPQBOOK9911015684903321Mean Field Guided Machine Learning4408719UNINA01273nam0 22003613i 450 UBO003375220251003044417.00471080799047183743119921204d1986 ||||0itac50 baengusz01i xxxe z01nz01ncRDAcarrierStatistics and data analysis in geologyJohn C. Davis2. edNew York [etc.]Wiley©1986X, 646 p.25 cm1 floppy disk.StatisticaFIRCFIC000038E519.5PROBABILITA' E MATEMATICA APPLICATA. STATISTICA MATEMATICA14Scienze statisticheStatisticaScienze statisticheDavis, John C.VEAV030817070204476Davis, John ClementsRT1V015795Davis, John C.ITIT-00000019921204IT-BN0095 NAP 01SALA $UBO0033752Biblioteca Centralizzata di Ateneo1 v. 01SALA 550.72 DAV.st 0104 9000003685 VMA 1 v.A 2008020520080205 01Statistics and data analysis in geology108614UNISANNIO