LEADER 02257nam 2200649 a 450 001 9910451909003321 005 20210107013417.0 010 $a1-59332-308-5 035 $a(CKB)1000000000481793 035 $a(OCoLC)608569800 035 $a(CaPaEBR)ebrary10225126 035 $a(SSID)ssj0000106364 035 $a(PQKBManifestationID)11125154 035 $a(PQKBTitleCode)TC0000106364 035 $a(PQKBWorkID)10106667 035 $a(PQKB)10463512 035 $a(MiAaPQ)EBC3016816 035 $a(EXLCZ)991000000000481793 100 $a20150424d2007|||| s|| | 101 0 $aeng 181 $ctxt 182 $cc 183 $acr 200 10$aAsian American Assimilation : Ethnicity, Immigration, and Socioeconomic Attainment$b[electronic resource] 210 $aNew York, NY, USA$cLFB Scholarly Publishing LLC$d20070701 210 $cLFB Scholarly Publishing LLC 215 $a1 online resource (257 p.) 225 0 $aThe new Americans Asian American assimilation 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-59332-175-9 606 $aSOCIAL SCIENCE$2bisac 606 $aEmigration & Immigration$2bisac 606 $aAsian Americans$xCultural assimilation$zUnited States 606 $aAsian Americans$xEthnic identity 606 $aImmigrants$xSocial conditions 606 $aAsian Americans$xSocial conditions 606 $aAsian Americans$xEconomic conditions 606 $aGender & Ethnic Studies$2HILCC 606 $aSocial Sciences$2HILCC 606 $aEthnic & Race Studies$2HILCC 615 7$aSOCIAL SCIENCE 615 7$aEmigration & Immigration 615 0$aAsian Americans$xCultural assimilation 615 0$aAsian Americans$xEthnic identity 615 0$aImmigrants$xSocial conditions 615 0$aAsian Americans$xSocial conditions 615 0$aAsian Americans$xEconomic conditions 615 7$aGender & Ethnic Studies 615 7$aSocial Sciences 615 7$aEthnic & Race Studies 676 $a305.895/073 700 $aLe$b C. N$0947429 801 0$bPQKB 906 $aBOOK 912 $a9910451909003321 996 $aAsian American Assimilation : Ethnicity, Immigration, and Socioeconomic Attainment$92140751 997 $aUNINA LEADER 04808nam 22005535 450 001 9910881096003321 005 20251225195113.0 010 $a3-031-67977-6 024 7 $a10.1007/978-3-031-67977-3 035 $a(MiAaPQ)EBC31608185 035 $a(Au-PeEL)EBL31608185 035 $a(CKB)34119666800041 035 $a(DE-He213)978-3-031-67977-3 035 $a(EXLCZ)9934119666800041 100 $a20240819d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBelief Functions: Theory and Applications $e8th International Conference, BELIEF 2024, Belfast, UK, September 2?4, 2024, Proceedings /$fedited by Yaxin Bi, Anne-Laure Jousselme, Thierry Denoeux 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (0 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v14909 311 08$a3-031-67976-8 320 $aIncludes bibliographical references and index. 327 $a -- Machine learning. -- Deep evidential clustering of images. -- Incremental Belief-peaks Evidential Clustering. -- Imprecise Deep Networks for Uncertain Image Classification. -- Dempster-Shafer Credal Probabilistic Circuits. -- Uncertainty quantification in regression neural networks using likelihood-based belief functions. -- An evidential time-to-event prediction model based on Gaussian random fuzzy numbers. -- Object Hallucination Detection in Large Vision Language Models via Evidential Conflict. -- Multi-oversampling with evidence fusion for imbalanced data classification. -- An Evidence-based Framework For Heterogeneous Electronic Health Records: A Case Study In Mortality Prediction. -- Conflict Management in a Distance to Prototype-Based Evidential Deep Learning. -- A Novel Privacy Preserving Framework for Training Dempster-Shafer Theory-based Evidential Deep Neural Network. -- Statistical inference. -- Large-sample theory for inferential models: A possibilistic Bernstein?von Mises theorem. -- Variational approximations of possibilistic inferential models. -- Decision theory via model-free generalized fiducial inference. -- Which statistical hypotheses are afflicted with false confidence?. -- Algebraic expression for the relative likelihood-based evidential prediction of an ordinal variable. -- Information fusion and optimization. -- Why Combining Belief Functions on Quantum Circuits?. -- SHADED: Shapley Value-based Deceptive Evidence Detection in Belief Functions. -- A Novel Optimization-Based Combination Rule for Dempster-Shafer Theory. -- Fusing independent inferential models in a black-box manner. -- Optimization under Severe Uncertainty: a Generalized Minimax Regret Approach for Problems with Linear Objectives. -- Measures of uncertainty, conflict and distances. -- A mean distance between elements of same class for rich labels. -- Threshold Functions and Operations in the Theory of Evidence. -- Mutual Information and Kullback-Leibler Divergence in the Dempster-Shafer Theory. -- An OWA-based Distance Measure for Ordered Frames of Discernment. -- Automated Hierarchical Conflict Reduction for Crowdsourced Annotation Tasks using Belief Functions. -- Continuous belief functions, logics, computation. -- Gamma Belief Functions. -- Combination of Dependent Gaussian Random Fuzzy Numbers. -- A 3-valued Logical Foundation for Evidential Reasoning. -- Accelerated Dempster Shafer using Tensor Train Representation. 330 $aThis book constitutes the refereed proceedings of the 8th International Conference on Belief Functions, BELIEF 2024, held in Belfast, UK, in September 2?4, 2024. The 30 full papers presented in this book were carefully selected and reviewed from 36 submissions. The papers cover a wide range on theoretical aspects on Machine learning; Statistical inference; Information fusion and optimization; Measures of uncertainty, conflict and distances; Continuous belief functions, logics, computation. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v14909 606 $aArtificial intelligence 606 $aProbabilities 606 $aArtificial Intelligence 606 $aProbability Theory 615 0$aArtificial intelligence. 615 0$aProbabilities. 615 14$aArtificial Intelligence. 615 24$aProbability Theory. 676 $a658.403 702 $aBi$b Yaxin 702 $aJousselme$b Anne-Laure 702 $aDenoeux$b Thierry 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910881096003321 996 $aBelief Functions: Theory and Applications$92154579 997 $aUNINA