LEADER 04280nam 22007335 450 001 996466466803316 005 20200701202046.0 010 $a3-030-03341-4 024 7 $a10.1007/978-3-030-03341-5 035 $a(CKB)4100000007110929 035 $a(DE-He213)978-3-030-03341-5 035 $a(MiAaPQ)EBC6284248 035 $a(PPN)232470693 035 $a(EXLCZ)994100000007110929 100 $a20181101d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPattern Recognition and Computer Vision$b[electronic resource] $eFirst Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part IV /$fedited by Jian-Huang Lai, Cheng-Lin Liu, Xilin Chen, Jie Zhou, Tieniu Tan, Nanning Zheng, Hongbin Zha 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XV, 434 p. 311 illus., 187 illus. in color.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11259 311 $a3-030-03340-6 327 $aBiometrics -- Computer Vision Application -- Deep Learning -- Document Analysis -- Face Recognition and Analysis -- Feature Extraction and Selection -- Machine Learning -- Object Detection and Tracking -- Performance Evaluation and Database -- Remote Sensing. 330 $aThe four-volume set LNCS 11056, 110257, 11258, and 11073 constitutes the refereed proceedings of the First Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018, held in Guangzhou, China, in November 2018. The 179 revised full papers presented were carefully reviewed and selected from 399 submissions. The papers have been organized in the following topical sections: Part I: Biometrics, Computer Vision Application. Part II: Deep Learning. Part III: Document Analysis, Face Recognition and Analysis, Feature Extraction and Selection, Machine Learning. Part IV: Object Detection and Tracking, Performance Evaluation and Database, Remote Sensing. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11259 606 $aOptical data processing 606 $aPattern recognition 606 $aArtificial intelligence 606 $aArithmetic and logic units, Computer 606 $aComputer organization 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aArithmetic and Logic Structures$3https://scigraph.springernature.com/ontologies/product-market-codes/I12026 606 $aComputer Systems Organization and Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13006 615 0$aOptical data processing. 615 0$aPattern recognition. 615 0$aArtificial intelligence. 615 0$aArithmetic and logic units, Computer. 615 0$aComputer organization. 615 14$aImage Processing and Computer Vision. 615 24$aPattern Recognition. 615 24$aArtificial Intelligence. 615 24$aArithmetic and Logic Structures. 615 24$aComputer Systems Organization and Communication Networks. 676 $a006.4 702 $aLai$b Jian-Huang$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLiu$b Cheng-Lin$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aChen$b Xilin$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZhou$b Jie$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTan$b Tieniu$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZheng$b Nanning$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZha$b Hongbin$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466466803316 996 $aPattern recognition and computer vision$91972598 997 $aUNISA 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