LEADER 00831nac0-22002771i-450- 001 990007367150403321 005 20021112 035 $a000736715 035 $aFED01000736715 035 $a(Aleph)000736715FED01 035 $a000736715 100 $a20021112d1901----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay-------000yy 200 1 $aDi un nuovo contributo alla riforma legislativa sulla precedenza del matrimonio civile$fS. Iannuzzi 210 $aNapoli$cTip. Gazz. Diritto e giurisprudenza$d1901 215 $a39 p.$d24 cm 300 $aEstratto dalla Gazzetta "Diritto e giurisprudenza", anno XVI, 1900-1901 700 1$aIannuzzi,$bS. 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990007367150403321 952 $aBUSTA 3[12] 13$b5559$fFGBC 959 $aFGBC 997 $aUNINA LEADER 01404nam 2200337 n 450 001 996396946603316 005 20221108033607.0 035 $a(CKB)4330000000332603 035 $a(EEBO)2240870370 035 $a(UnM)99844887 035 $a(EXLCZ)994330000000332603 100 $a19910912d1631 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 04$aThe nevv inne. Or, The light heart$b[electronic resource] $eA comoedy. As it was neuer acted, but most negligently play'd, by some, the Kings Seruants. And more squeamishly beheld, and censured by others, the Kings subiects. 1629. Now, at last, set at liberty to the readers, his Maties seruants, and subiects, to be iudg'd. 1631. By the author, B. Ionson 210 $aLondon $cPrinted by Thomas Harper, for Thomas Alchorne, and are to be sold at his shop in Pauls Church-yeard, at the signe of the greene Dragon$dMDCXXXI. [1631] 215 $a[120] p 300 $aPartly in verse. 300 $aSignatures: (*) A² B-G (G7 + H² ). 300 $aThe last leaf is blank. 300 $aReproduction of the original in the British Library. 330 $aeebo-0018 700 $aJonson$b Ben$f1573?-1637.$0445907 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 801 2$bWaOLN 906 $aBOOK 912 $a996396946603316 996 $aThe nevv inne. Or, The light heart$92349415 997 $aUNISA LEADER 03950nam 22006015 450 001 9911015684903321 005 20250712072154.0 010 $a3-031-91859-2 024 7 $a10.1007/978-3-031-91859-9 035 $a(MiAaPQ)EBC32202187 035 $a(Au-PeEL)EBL32202187 035 $a(CKB)39620867300041 035 $a(OCoLC)1527722694 035 $a(DE-He213)978-3-031-91859-9 035 $a(EXLCZ)9939620867300041 100 $a20250709d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMean Field Guided Machine Learning /$fby Yuhan Kang, Hao Gao, Zhu Han 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (248 pages) 225 1 $aWireless Networks,$x2366-1445 311 08$a3-031-91858-4 327 $aPreface -- 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. 330 $aThis book explores the integration of Mean Field Game (MFG) theory with machine learning (ML), presenting both theoretical foundations and practical applications. Drawing from extensive research, it provides insights into how MFG can improve various ML techniques, including supervised learning, reinforcement learning, and federated learning. MFG theory and ML are converging to address critical challenges in high-dimensional spaces and multi-agent systems. While ML has transformed industries by leveraging vast data and computational power, scalability and robustness remain key concerns. MFG theory, which models large populations of interacting agents, offers a mathematical framework to simplify and optimize complex systems, enhancing ML?s efficiency and applicability. By bridging these two fields, this book aims to drive innovation in scalable and robust machine learning. The integration of MFG with ML not only expands research possibilities but also paves the way for more adaptive and intelligent systems. Through this work, the authors hope to inspire further exploration and development in this promising interdisciplinary domain. With case studies and real-world examples, this book serves as a guide for researchers and students in communications and networks seeking to harness MFG?s potential in advancing ML. Industry managers, practitioners and government research workers in the fields of communications and networks will find this book a valuable resource as well. 410 0$aWireless Networks,$x2366-1445 606 $aMachine learning 606 $aTelecommunication 606 $aArtificial intelligence 606 $aMachine Learning 606 $aCommunications Engineering, Networks 606 $aArtificial Intelligence 615 0$aMachine learning. 615 0$aTelecommunication. 615 0$aArtificial intelligence. 615 14$aMachine Learning. 615 24$aCommunications Engineering, Networks. 615 24$aArtificial Intelligence. 676 $a006.31 700 $aKang$b Yuhan$01833765 701 $aGao$b Hao$01064416 701 $aHan$b Zhu$0732360 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911015684903321 996 $aMean Field Guided Machine Learning$94408719 997 $aUNINA