Fonctions de croyance : décompositions canoniques et règles de combinaison. (Belief functions: canonical decompositions and combination rules)
暂无分享,去创建一个
[1] Arie Tzvieli. Possibility theory: An approach to computerized processing of uncertainty , 1990, J. Am. Soc. Inf. Sci..
[2] Thierry Denoeux. The cautious rule of combination for belief functions and some extensions , 2006, 2006 9th International Conference on Information Fusion.
[3] Philippe Smets,et al. Information Content of an Evidence , 1983, Int. J. Man Mach. Stud..
[4] Marc Pouly,et al. A generic Architecture for local Computation , 2022 .
[5] Thierry Denoeux,et al. Constructing belief functions from sample data using multinomial confidence regions , 2006, Int. J. Approx. Reason..
[6] Didier Dubois,et al. Joint propagation of probability and possibility in risk analysis: Towards a formal framework , 2007, Int. J. Approx. Reason..
[7] G. Cooman,et al. Lower previsions induced by multi-valued mappings , 2005 .
[8] R. Yager. An introduction to applications of possibility theory (+ commentaries by L.A. Zadeh, W. Bandler, T. Saaty, A. Kandel, D. Dubois & H. Prade, R.M. Tong and M. Kochen) , 1982 .
[9] Arthur P. Dempster,et al. Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.
[10] Johan Schubert. Clustering decomposed belief functions using generalized weights of conflict , 2008, Int. J. Approx. Reason..
[11] Prakash P. Shenoy,et al. On the plausibility transformation method for translating belief function models to probability models , 2006, Int. J. Approx. Reason..
[12] Philippe Smets,et al. Decision making in the TBM: the necessity of the pignistic transformation , 2005, Int. J. Approx. Reason..
[13] Michel Grabisch,et al. Using the transferable belief model and a qualitative possibility theory approach on an illustrative example: The assessment of the value of a candidate * , 2001, Int. J. Intell. Syst..
[14] Matthew L. Ginsberg,et al. Non-Monotonic Reasoning Using Dempster's Rule , 1984, AAAI.
[15] G. Choquet. Theory of capacities , 1954 .
[16] Thierry Denoeux,et al. On Latent Belief Structures , 2007, ECSQARU.
[17] Henri Prade,et al. Representation and combination of uncertainty with belief functions and possibility measures , 1988, Comput. Intell..
[18] F. Pichon,et al. T-norm and uninorm-based combination of belief functions , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.
[19] Philippe Smets,et al. Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem , 1993, Int. J. Approx. Reason..
[20] Petr Hájek,et al. Uncertain information processing in expert systems , 1992 .
[21] Marco E. G. V. Cattaneo. Combining Belief Functions Issued from Dependent Sources , 2003, ISIPTA.
[22] Philippe Smets,et al. The Canonical Decomposition of a Weighted Belief , 1995, IJCAI.
[23] Thierry Denoeux,et al. A New Justification of the Unnormalized Dempster's Rule of Combination from the Least Commitment Principle , 2008, FLAIRS.
[24] H. Prade,et al. La fusion d'informations imprécises , 1994 .
[25] Minh Ha-Duong. Hierarchical fusion of expert opinions in the Transferable Belief Model, application to climate sensitivity , 2008, Int. J. Approx. Reason..
[26] Thierry Denoeux,et al. Modeling vague beliefs using fuzzy-valued belief structures , 2000, Fuzzy Sets Syst..
[27] Philippe Smets. The alpha-junctions: Combination Operators Applicable to Belief Functions , 1997, ECSQARU-FAPR.
[28] Serafín Moral Callejón. Información difusa: relaciones entre probabilidad y posibilidad , 1985 .
[29] Philippe Smets,et al. The Transferable Belief Model for Quantified Belief Representation , 1998 .
[30] Ivan Kramosil. Probabilistic analysis of belief functions , 2001 .
[31] Dov M. Gabbay,et al. Handbook of defeasible reasoning and uncertainty management systems: volume 2: reasoning with actual and potential contradictions , 1998 .
[32] Didier Dubois,et al. On the use of aggregation operations in information fusion processes , 2004, Fuzzy Sets Syst..
[33] Philippe Smets,et al. BELIEF FUNCTIONS AND THE TRANSFERABLE BELIEF MODEL , 2000 .
[34] Frank Klawonn,et al. On the axiomatic justification of Dempster's rule of combination , 1992, Int. J. Intell. Syst..
[35] Bloch. 1 - Incertitude, imprécision et additivité en fusion de données : point de vue historique , 1996 .
[36] D. Dubois,et al. A set-theoretic view of belief functions: Logical operations and approximations by fuzzy sets , 1986 .
[37] Didier Dubois,et al. Cautious Conjunctive Merging of Belief Functions , 2007, ECSQARU.
[38] Rudolf Kruse,et al. The Transferable Belief Model for Belief Representation , 1996, Uncertainty Management in Information Systems.
[39] Thierry Denoeux,et al. An evidence-theoretic k-NN rule with parameter optimization , 1998, IEEE Trans. Syst. Man Cybern. Part C.
[40] G. Rota. On the foundations of combinatorial theory I. Theory of Möbius Functions , 1964 .
[41] Didier Dubois,et al. Possibility theory in information fusion , 2002, NMR.
[42] Philippe Smets,et al. Computational aspects of the Mobius transformation , 1990, UAI.
[43] Philippe Smets,et al. Data association in multi‐target detection using the transferable belief model , 2001, Int. J. Intell. Syst..
[44] Petr Hájek,et al. On Belief Functions , 1992, Advanced Topics in Artificial Intelligence.
[45] P. Smets. Managing deceitful reports with the transferable belief model , 2005, 2005 7th International Conference on Information Fusion.
[46] T. Denœux. Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence , 2008 .
[47] Frédéric Pichon. A new singular property of the unnormalized Dempster's rule among uninorm-based combination rules , 2008 .
[48] Rolf Haenni,et al. Uncover Dempster's Rule Where It Is Hidden , 2006, 2006 9th International Conference on Information Fusion.
[49] Christophe Labreuche,et al. Modeling Positive and Negative Pieces of Evidence in Uncertainty , 2003, ECSQARU.
[50] Ronald R. Yager,et al. Uninorm aggregation operators , 1996, Fuzzy Sets Syst..
[51] Philippe Smets,et al. Belief functions on real numbers , 2005, Int. J. Approx. Reason..
[52] Thierry Denoeux,et al. Adapting a Combination Rule to Non-Independent Information Sources , 2008, IPMU 2008.
[53] Khaled Mellouli,et al. Pooling dependent expert opinions using the theory of evidence , 1998 .
[54] Leo Egghe,et al. Uncertainty and information: Foundations of generalized information theory , 2007, J. Assoc. Inf. Sci. Technol..
[55] Philippe Smets,et al. What is Dempster-Shafer's model? , 1994 .
[56] David J. Spiegelhalter,et al. Local computations with probabilities on graphical structures and their application to expert systems , 1990 .
[57] Philippe Smets,et al. The Transferable Belief Model , 1991, Artif. Intell..
[58] Thierry DENCEUX,et al. Application du modèle des croyances transférables en reconnaissance de formes , 2006 .
[59] L. Zadeh. Fuzzy sets as a basis for a theory of possibility , 1999 .
[60] Amihai Motro,et al. Uncertainty Management in Information Systems: From Needs to Solution , 1996 .
[61] Thierry Denoeux,et al. Decision fusion for postal address recognition using belief functions , 2009, Expert Syst. Appl..
[62] Didier Dubois,et al. New Semantics for Quantitative Possibility Theory , 2001, ECSQARU.
[63] Prakash P. Shenoy,et al. Axioms for probability and belief-function proagation , 1990, UAI.
[64] Frank Klawonn,et al. The Dynamic of Belief in the Transferable Belief Model and Specialization-Generalization Matrices , 1992, UAI.
[65] Glenn Shafer,et al. A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.
[66] Didier Dubois,et al. On the unicity of dempster rule of combination , 1986, Int. J. Intell. Syst..
[67] Michel Grabisch,et al. Belief functions on lattices , 2008, Int. J. Intell. Syst..
[68] P. Smets,et al. Target identification using belief functions and implication rules , 2005, IEEE Transactions on Aerospace and Electronic Systems.
[69] Philippe Smets,et al. The Combination of Evidence in the Transferable Belief Model , 1990, IEEE Trans. Pattern Anal. Mach. Intell..
[70] Ivan Kramosil. Measure-theoretic approach to the inversion problem for belief functions , 1999, Fuzzy Sets Syst..
[71] R. Yager. On the dempster-shafer framework and new combination rules , 1987, Inf. Sci..
[72] Sanjay Modgil,et al. 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 07) , 2007 .
[73] par Isabelle Bloc. Incertitude , imprécision et additivité en fusion de données : point de vue historique Uncertainty , Imprecision and Additivity in Data Fusion : Historical Point of View , 2006 .
[74] G. Rota. On the Foundations of Combinatorial Theory , 2009 .
[75] P. Walley. Statistical Reasoning with Imprecise Probabilities , 1990 .
[76] P. Hájek,et al. A generalized algebraic approach to uncertainty processing in rule-based expert systems (dempsteroids) , 1991 .
[77] Hong Xu,et al. Generating Explanations for Evidential Reasoning , 1995, UAI.
[78] K. Mellouli,et al. Constructing Belief Functions from Qualitative Expert Opinions , 2006, 2006 2nd International Conference on Information & Communication Technologies.
[79] Ronald R. Yager,et al. The entailment principle for dempster—shafer granules , 1986, Int. J. Intell. Syst..
[80] Prakash P. Shenoy,et al. Computation in Valuation Algebras , 2000 .
[81] Philippe Smets,et al. Imperfect Information: Imprecision and Uncertainty , 1996, Uncertainty Management in Information Systems.
[82] Philippe Smets,et al. Analyzing the combination of conflicting belief functions , 2007, Inf. Fusion.
[83] Thierry Denoeux,et al. Refined classifier combination using belief functions , 2008, 2008 11th International Conference on Information Fusion.
[84] QuostBenjamin,et al. Pairwise classifier combination using belief functions , 2007 .
[85] Thierry Denoeux,et al. Refined modeling of sensor reliability in the belief function framework using contextual discounting , 2008, Inf. Fusion.
[86] Madan M. Gupta,et al. Approximate reasoning in expert systems , 1985 .
[87] Prakash P. Shenoy,et al. Conditional independence in valuation-based systems , 1994, Int. J. Approx. Reason..
[88] Didier Dubois,et al. "Not Impossible" vs. "Guaranteed Possible" in Fusion and Revision , 2001, ECSQARU.
[89] Kari Sentz,et al. Combination of Evidence in Dempster-Shafer Theory , 2002 .
[90] M. Masson,et al. Fusion of multi-level decision systems using the transferable belief model , 2005, 2005 7th International Conference on Information Fusion.