Belief Functions and Pattern recognition

  1. M.-H. Masson and T. Denoeux. Ensemble clustering in the belief functions framework. International Journal of Approximate Reasoning, Accepted for publication, 2010. pdf
  2. G. Nassreddine, F. Abdallah and T. Denoeux. State estimation using interval analysis and belief function theory: Application to dynamic vehicle localization. IEEE Transactions on Systems, Man and Cybernetics B, Accepted for publication, 2009. pdf
  3. F. Pichon and T. Denoeux. The unnormalized Dempster’s rule of combination: a new justi cation from the Least Commitment Principle and some extensions. Journal of Automated Reasoning, Vol. 45, Issue 1, pages 61-87, 2010. pdf
  4. T. Denoeux, Z. Younes and F. Abdallah. Representing uncertainty on set-valued variables using belief functions. Artificial Intelligence, Vol. 174, Issues 7-8, pages 479-499, 2010. pdf
  5. M.-H. Masson and T. Denoeux. RECM: Relational Evidential c-means algorithm. Pattern Recognition Letters, Vol. 30, pages 1015-1026, 2009. pdf
  6. T. Denoeux. Extending stochastic ordering to belief functions on the real line. Information Sciences, Vol. 179, pages 1362-1376, 2009. pdf
  7. E. Côme, L. Oukhellou, T. Denoeux and P. Aknin. Learning from partially supervised data using mixture models and belief functions. Pattern Recognition, Vol. 42, Issue 3, pages 334-348, 2009. pdf
  8. A. Aregui and T. Denoeux. Constructing Consonant Belief Functions from Sample Data using Confidence Sets of Pignistic Probabilities. International Journal of Approximate Reasoning, vol. 49, Issue 3, pages 575–594, 2008. pdf
  9. M.-H. Masson and T. Denoeux. ECM: An evidential version of the fuzzy c-means algorithm. Pattern Recognition, Vol. 41, Issue 4, pages 1384– 1397, 2008. pdf
  10. T. Denoeux. Conjunctive and Disjunctive Combination of Belief Functions Induced by Non Distinct Bodies of Evidence. Artificial Intelligence, Vol. 172, pages 234–264, 2008. pdf
  11. D. Mercier, B. Quost and T. Denoeux. Refined modeling of sensor reliability in the belief function framework using contextual discounting, Information Fusion, Vol. 9, pages 246–258, 2008. pdf
  12. B. Quost, T. Denoeux and M.-H. Masson. Combinaison crédibiliste de classifieurs binaires. Traitement du Signal, Vol. 24, Issue 2, pages 83-101, 2007. pdf
  13. B. Quost, T. Denoeux and M.-H. Masson. Pairwise classifier combination using belief functions. Pattern Recognition Letters, Volume 28, Issue 5 , Pages 644-653, 2007. pdf
  14. T. Denoeux and P. Smets. Classification using Belief Functions: the Relationship between the Case-based and Model-based Approaches, IEEE Transactions on Systems, Man and Cybernetics B , Vol. 36, Issue 6, Pages 1395-1406, 2006. pdf
  15. T. Denoeux. Constructing Belief Functions from Sample Data Using Multinomial Confidence Regions. International Journal of Approximate Reasoning, Vol. 42, Issue 3, Pages 228-252, 2006. pdf
  16. M.-H. Masson and T. Denoeux. Clustering Interval-valued Data using Belief Functions. Pattern Recognition Letters, Vol. 25, Issue 2, 2004, Pages 163-171. postscript
  17. T. Denoeux and M.-H. Masson. EVCLUS: Evidential Clustering of Proximity Data. IEEE Transactions on Systems, Man and Cybernetics B, Vol. 34, Issue 1, 95-109, 2004. pdf
  18. S. Petit-Renaud and T. Denoeux. Nonparametric regression analysis of uncertain and imprecise data using belief Functions. International Journal of Approximate Reasoning, Vol. 35, No. 1, 1-28, 2004. postscript
  19. J. François, Y. Grandvalet, T. Denoeux and J.-M. Roger. Resample and Combine: An Approach to Improving Uncertainty Representation in Evidential Pattern Classification. Information Fusion, (4):75-85, 2003. postscript
  20. T. Denoeux and A. Ben Yaghlane. Approximating the Combination of Belief Functions using the Fast Moebius Transform in a coarsened frame. International Journal of Approximate Reasoning, Vol. 31, No. 1-2, 77-101, 2002. pdf
  21. T. Denoeux. Inner and outer approximation of belief structures using a hierarchical clustering approach. Int. Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 9, No. 4, 437-460, 2001. postscript
  22. T. Denoeux and L. M. Zouhal. Handling possibilistic labels in pattern classification using evidential reasoning. Fuzzy Sets and Systems, 122(3):47-62, 2001. postscript
  23. T. Denoeux. Modeling vague beliefs using fuzzy-valued belief structures. Fuzzy Sets and Systems, 116(2):167-199, 2000. postscript
  24. T. Denoeux. A neural network classifier based on Dempster-Shafer theory. IEEE transactions on Systems, Man and Cybernetics A, 30(2):131-150, 2000. pdf
  25. T. Denoeux. Reasoning with imprecise belief structures. International Journal of Approximate Reasoning, 20:79-111, 1999. postscript
  26. L. M. Zouhal and T. Denoeux. An evidence-theoretic k-NN rule with parameter optimization. IEEE Transactions on Systems, Man and Cybernetics - Part C, 28(2):263-271,1998. pdf
  27. T. Denoeux, M. Masson and B. Dubuisson. Advanced pattern recognition techniques for system monitoring and diagnosis: a survey. Journal Européen des Systèmes Automatisés (RAIRO-APII-JESA), 31(9-10):1509-1539, 1998. ps
  28. T. Denoeux. Application du modèle des Croyances Transférables en Reconnaissance de Formes. Traitement du Signal, 14(5):443-451, 1998. postscript
  29. T. Denoeux. Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recognition, 30(7):1095-1107, 1997. postscript
  30. T. Denoeux. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics, 25(05):804-813, 1995. pdf
 
en/publi/belief_art.txt · Last modified: 05/05/2010 09:36 by tdenoeux
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