001450020 000__ 05953cam\a2200637\i\4500
001450020 001__ 1450020
001450020 003__ OCoLC
001450020 005__ 20230310004504.0
001450020 006__ m\\\\\o\\d\\\\\\\\
001450020 007__ cr\un\nnnunnun
001450020 008__ 221005s2022\\\\sz\a\\\\o\\\\\101\0\eng\d
001450020 019__ $$a1346535349
001450020 020__ $$a9783031178016$$q(electronic bk.)
001450020 020__ $$a3031178017$$q(electronic bk.)
001450020 020__ $$z9783031178009$$q(print)
001450020 020__ $$z3031178009
001450020 0247_ $$a10.1007/978-3-031-17801-6$$2doi
001450020 035__ $$aSP(OCoLC)1346621362
001450020 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dOCLCF$$dUKAHL
001450020 049__ $$aISEA
001450020 050_4 $$aT57.95$$b.I582 2022eb
001450020 08204 $$a658.4/03$$223
001450020 1112_ $$aInternational Conference on Belief Functions$$n(7th :$$d2022 :$$cParis, France ; Online)
001450020 24510 $$aBelief functions :$$btheory and applications : 7th International Conference, BELIEF 2022, Paris, France, October 26-28, 2022, Proceedings /$$cSylvie Le Hégarat-Mascle, Isabelle Bloch, Emanuel Aldea (eds.).
001450020 2463_ $$aBELIEF 2022
001450020 264_1 $$aCham :$$bSpringer,$$c2022.
001450020 300__ $$a1 online resource (xi, 317 pages) :$$billustrations (some color).
001450020 336__ $$atext$$btxt$$2rdacontent
001450020 337__ $$acomputer$$bc$$2rdamedia
001450020 338__ $$aonline resource$$bcr$$2rdacarrier
001450020 4901_ $$aLecture notes in artificial intelligence
001450020 4901_ $$aLecture notes in computer science ;$$v13506
001450020 4901_ $$aLNCS sublibrary, SL 7, Artificial intelligence
001450020 500__ $$aIncludes author index.
001450020 5050_ $$aEvidential Clustering A Distributional Approach for Soft Clustering Comparison and Evaluation -- Causal transfer evidential clustering -- Jiang A variational Bayesian clustering approach to acoustic emission interpretation including soft labels -- Evidential clustering by Competitive Agglomeration -- Imperfect Labels with Belief Functions for Active Learning -- Machine Learning and Pattern Recognition An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers -- Ordinal Classification using Single-model Evidential Extreme Learning Machine -- Reliability-based imbalanced data classification with Dempster-Shafer theory -- Evidential regression by synthesizing feature selection and parameters learning -- Algorithms and Evidential Operators Distributed EK-NN classification -- On improving a group of evidential sources with different contextual corrections -- Measure of Information Content of Basic Belief Assignments -- Belief functions on On Modelling and Solving the Shortest Path Problem with Evidential Weights -- Data and Information Fusion Heterogeneous Image Fusion for Target Recognition based on Evidence Reasoning -- Cluster Decomposition of the Body of Evidence -- Evidential Trustworthiness Estimation for Cooperative Perception -- An Intelligent System for Managing Uncertain Temporal Flood events -- Statistical Inference - Graphical Models A practical strategy for valid partial prior-dependent possibilistic inference -- On Conditional Belief Functions in the Dempster-Shafer Theory -- Valid inferential models offer performance and probativeness assurances.Links with Other Uncertainty Theories A qualitative counterpart of belief functions with application to uncertainty propagation in safety cases -- The Extension of Dempster's Combination Rule Based on Generalized Credal Sets -- A Correspondence between Credal Partitions and Fuzzy Orthopartitions -- Toward updating belief functions over Belnap-Dunn logic -- Applications Real bird dataset with imprecise and uncertain values -- Addressing ambiguity in randomized reinsurance contracts using belief functions -- Evidential filtering and spatio-temporal gradient for micro-movements analysis in the context of bedsores prevention -- Hybrid Artificial Immune Recognition System with improved belief classification process.
001450020 506__ $$aAccess limited to authorized users.
001450020 520__ $$aThis book constitutes the refereed proceedings of the 7th International Conference on Belief Functions, BELIEF 2022, held in Paris, France, in October 2022. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well-understood connections to other frameworks such as probability, possibility, and imprecise probability theories. It has been applied in diverse areas such as machine learning, information fusion, and pattern recognition. The 29 full papers presented in this book were carefully selected and reviewed from 31 submissions. The papers cover a wide range on theoretical aspects on mathematical foundations, statistical inference as well as on applications in various areas including classification, clustering, data fusion, image processing, and much more.
001450020 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 5, 2022).
001450020 650_0 $$aDecision making$$xData processing$$vCongresses.
001450020 650_0 $$aDecision making$$xMathematical models$$vCongresses.
001450020 650_0 $$aUncertainty (Information theory)$$vCongresses.
001450020 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001450020 655_0 $$aElectronic books.
001450020 7001_ $$aLe Hégarat-Mascle, Sylvie,$$eeditor.$$0(orcid)0000-0001-8494-2289$$1https://orcid.org/0000-0001-8494-2289
001450020 7001_ $$aBloch, Isabelle,$$eeditor.$$1https://orcid.org/0000-0002-6984-1532
001450020 7001_ $$aAldea, Emanuel,$$eeditor.$$0(orcid)0000-0001-7065-4809$$1https://orcid.org/0000-0001-7065-4809
001450020 77608 $$iPrint version: $$z3031178009$$z9783031178009$$w(OCoLC)1342620690
001450020 830_0 $$aLecture notes in computer science.$$pLecture notes in artificial intelligence.
001450020 830_0 $$aLecture notes in computer science ;$$v13506.
001450020 830_0 $$aLNCS sublibrary.$$nSL 7,$$pArtificial intelligence.
001450020 852__ $$bebk
001450020 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-17801-6$$zOnline Access$$91397441.1
001450020 909CO $$ooai:library.usi.edu:1450020$$pGLOBAL_SET
001450020 980__ $$aBIB
001450020 980__ $$aEBOOK
001450020 982__ $$aEbook
001450020 983__ $$aOnline
001450020 994__ $$a92$$bISE