Symbolic and Quantitative Approaches to Reasoning with Uncertainty
暂无分享,去创建一个
Odile Papini | Alessandro Antonucci | Laurence Cholvy | Odile Papini | Alessandro Antonucci | L. Cholvy
[1] Didier Dubois,et al. Weighted logics for artificial intelligence - an introductory discussion , 2014, Int. J. Approx. Reason..
[2] Didier Dubois,et al. A Two-Tiered Propositional Framework for Handling Multisource Inconsistent Information , 2017, ECSQARU.
[3] Jure Leskovec,et al. Learning to Discover Social Circles in Ego Networks , 2012, NIPS.
[4] Weiru Liu,et al. The basic principles of uncertain information fusion. An organised review of merging rules in different representation frameworks , 2016, Inf. Fusion.
[5] Giuseppe Sanfilippo,et al. Square of Opposition Under Coherence , 2016, SMPS.
[6] John Riedl,et al. Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.
[7] Didier Dubois,et al. A simple logic for reasoning about incomplete knowledge , 2014, Int. J. Approx. Reason..
[8] Michel Grabisch,et al. Set Functions, Games and Capacities in Decision Making , 2016 .
[9] Yves Lepage,et al. Analogy and Formal Languages , 2004, FGMOL.
[10] Giuseppe Sanfilippo,et al. Probabilistic squares and hexagons of opposition under coherence , 2017, Int. J. Approx. Reason..
[11] Philippe Smets. Application of the transferable belief model to diagnostic problems , 1998, Int. J. Intell. Syst..
[12] Arthur P. Dempster,et al. A Generalization of Bayesian Inference , 1968, Classic Works of the Dempster-Shafer Theory of Belief Functions.
[13] P. Jones,et al. Inferring missing links in partially observed social networks , 2009, J. Oper. Res. Soc..
[14] Didier Dubois,et al. Representation, Propagation, and Decision Issues in Risk Analysis Under Incomplete Probabilistic Information , 2010, Risk analysis : an official publication of the Society for Risk Analysis.
[15] Van-Nam Huynh,et al. A Reliably Weighted Collaborative Filtering System , 2015, ECSQARU.
[16] Taghi M. Khoshgoftaar,et al. Collaborative Filtering for Multi-Class Data Using Bayesian Networks , 2008, Int. J. Artif. Intell. Tools.
[17] Vann McGee,et al. Conditional Probabilities and Compounds of Conditionals , 1989 .
[18] Zhi-Dan Zhao,et al. User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.
[19] J. Bobadilla,et al. Recommender systems survey , 2013, Knowl. Based Syst..
[20] Van-Nam Huynh,et al. A Community-Based Collaborative Filtering System Dealing with Sparsity Problem and Data Imperfections , 2014, PRICAI.
[21] Sang-goo Lee,et al. Reversed CF: A fast collaborative filtering algorithm using a k-nearest neighbor graph , 2015, Expert Syst. Appl..
[22] Qiang Yang,et al. Scalable collaborative filtering using cluster-based smoothing , 2005, SIGIR '05.
[23] Glenn Shafer,et al. A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.
[24] Angelo Gilio,et al. A Generalized Probabilistic Version of Modus Ponens , 2017 .
[25] Thierry Denoeux,et al. ECM: An evidential version of the fuzzy c , 2008, Pattern Recognit..
[26] Didier Dubois,et al. Representing qualitative capacities as families of possibility measures , 2015, Int. J. Approx. Reason..
[27] Zied Elouedi,et al. Evidential Missing Link Prediction in Uncertain Social Networks , 2016, IPMU.
[28] Pontus Svenson. Social network analysis of uncertain networks , 2008 .
[29] Jean-Paul Delahaye,et al. Numerical evaluation of algorithmic complexity for short strings: A glance into the innermost structure of randomness , 2011, Appl. Math. Comput..
[30] Gilles Richard,et al. From Analogical Proportion to Logical Proportions , 2013, Logica Universalis.
[31] Didier Dubois,et al. Combined analysis of unique and repetitive events in quantitative risk assessment , 2016, Int. J. Approx. Reason..
[32] Steven Schockaert,et al. Reasoning about Uncertainty and Explicit Ignorance in Generalized Possibilistic Logic , 2014, ECAI.
[33] Ray J. Solomonoff,et al. A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..
[34] Philippe Smets,et al. The Canonical Decomposition of a Weighted Belief , 1995, IJCAI.
[35] Taghi M. Khoshgoftaar,et al. A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..
[36] Niki Pfeifer,et al. Inference in conditional probability logic , 2006, Kybernetika.
[37] Jean-Paul Delahaye,et al. Correspondence and Independence of Numerical Evaluations of Algorithmic Information Measures , 2012, Comput..
[38] Gilles Richard,et al. Homogenous and Heterogeneous Logical Proportions , 2014, FLAP.
[39] Zied Elouedi,et al. Evidential Item-Based Collaborative Filtering , 2016, KSEM.
[40] Khaled Mellouli,et al. Clustering Approach Using Belief Function Theory , 2006, AIMSA.
[41] Neil Yorke-Smith,et al. Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems , 2015, Knowl. Based Syst..
[42] Panagiotis Symeonidis. Matrix and Tensor Decomposition in Recommender Systems , 2016, RecSys.
[43] Niki Pfeifer,et al. The new psychology of reasoning: A mental probability logical perspective , 2013 .
[44] Sanjay Goel,et al. Kolmogorov complexity estimates for detection of viruses in biologically inspired security systems: A comparison with traditional approaches , 2003, Complex..
[45] Philippe Smets,et al. The Transferable Belief Model for Quantified Belief Representation , 1998 .
[46] Niki Pfeifer,et al. Framing human inference by coherence based probability logic , 2009, J. Appl. Log..
[47] T. Fine,et al. Varieties of modal (classificatory) and comparative probability , 1979, Synthese.
[48] Laurent Miclet,et al. Relation d'analogie et distance sur un alphabet défini par des traits , 2004 .
[49] Henri Prade,et al. Handling Analogical Proportions in Classical Logic and Fuzzy Logics Settings , 2009, ECSQARU.
[50] Barbara Vantaggi,et al. Envelopes of conditional probabilities extending a strategy and a prior probability , 2016, Int. J. Approx. Reason..
[51] Robert LIN,et al. NOTE ON FUZZY SETS , 2014 .
[52] François Yvon,et al. Du quatrième de proportion comme principe inductif : une proposition et son application à l’apprentissage de la morphologie [Inference with formal analogical proportions: application to the automatic learning of morphology] , 2006, TAL.
[53] Jia Zhang,et al. An effective collaborative filtering algorithm based on user preference clustering , 2016, Applied Intelligence.
[54] John Riedl,et al. Recommender Systems for Large-scale E-Commerce : Scalable Neighborhood Formation Using Clustering , 2002 .
[55] S. Ferson,et al. Different methods are needed to propagate ignorance and variability , 1996 .
[56] M. Newman. Clustering and preferential attachment in growing networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.
[57] Sang Joon Kim,et al. A Mathematical Theory of Communication , 2006 .