03093nam 2200673 a 450 991045309340332120200520144314.00-7022-4904-10-7022-4905-X(CKB)2550000001135571(EBL)1176977(OCoLC)841493942(SSID)ssj0001036465(PQKBManifestationID)12338537(PQKBTitleCode)TC0001036465(PQKBWorkID)11041990(PQKB)10778556(MiAaPQ)EBC1176977(Au-PeEL)EBL1176977(CaPaEBR)ebr10714667(CaONFJC)MIL535279(EXLCZ)99255000000113557120130612d2012 uy 0engur|n|---|||||txtccrEnding holy wars[electronic resource] religion and conflict resolution in civil wars /Isak SvenssonSt Lucia, Qld. UQP20121 online resource (233 p.)New approaches to peace and conflictDescription based upon print version of record.0-7022-4956-4 1-306-04028-0 Includes bibliographical references and index.Front Cover ; Author biography; Other titles in UQP's New Approaches to Peace and Conflict series; Title page; Imprint page; Dedication; Note from Series Editor; Contents; List of tables and graphs; Preface; Introduction; Chapter 1 - Where and when do religious conflicts occur?; Chapter 2 - Which religious conflicts are difficult to resolve?; Chapter 3 - How do religious issue conflicts get resolved through peace agreements?; Chapter 4 - How do religious issue conflicts get resolved through concessions?; Chapter 5 - Towards a theory of desacralisation; Chapter 6 - ImplicationsAcknowledgementsNotes; References; Index<P style=""MARGIN: 0in 0in 0pt"" class=MsoNormal>An exploration of how religious dimensions affect the possibilities for conflict resolution in civil war, this account systematically attempts to map out the religious dimensions of internal armed conflicts and to explain the conditions under which religious dimensions impede peaceful settlement. Drawing upon empirical work on global data, and based on the Uppsala Conflict Data Program, this book shows how religious identities and incompatibilities influence the likelihood of agreements and the mechanisms through which parties and third-party meNew approaches to peace and conflict.PeaceDiplomacyConflict managementCivil warElectronic books.Peace.Diplomacy.Conflict management.Civil war.201.7273299.00Svensson Isak879461MiAaPQMiAaPQMiAaPQBOOK9910453093403321Ending holy wars1963723UNINA03674nam 22006255 450 991063108370332120251009105848.09789811967030981196703210.1007/978-981-19-6703-0(MiAaPQ)EBC7141199(Au-PeEL)EBL7141199(CKB)25361047200041(PPN)266350712(DE-He213)978-981-19-6703-0(OCoLC)1351203976(EXLCZ)992536104720004120221115d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierLatent Factor Analysis for High-dimensional and Sparse Matrices A particle swarm optimization-based approach /by Ye Yuan, Xin Luo1st ed. 2022.Singapore :Springer Nature Singapore :Imprint: Springer,2022.1 online resource (99 pages)SpringerBriefs in Computer Science,2191-5776Print version: Yuan, Ye Latent Factor Analysis for High-Dimensional and Sparse Matrices Singapore : Springer,c2022 9789811967023 Includes bibliographical references and index.Chapter 1. Introduction -- Chapter 2. Learning rate-free Latent Factor Analysis via PSO -- Chapter 3. Learning Rate and Regularization Coefficient-free Latent Factor Analysis via PSO -- Chapter 4. Regularization and Momentum Coefficient-free Non-negative Latent Factor Analysis via PSO -- Chapter 5. Advanced Learning rate-free Latent Factor Analysis via P2SO -- Chapter 6. Conclusion and Discussion.Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question. This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications. The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.SpringerBriefs in Computer Science,2191-5776Artificial intelligenceData processingQuantitative researchData miningData ScienceData Analysis and Big DataData Mining and Knowledge DiscoveryArtificial intelligenceData processing.Quantitative research.Data mining.Data Science.Data Analysis and Big Data.Data Mining and Knowledge Discovery.519.535Yuan Ye875363Luo XinMiAaPQMiAaPQMiAaPQBOOK9910631083703321Latent factor analysis for high-dimensional and sparse matrices3083208UNINA