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Improving Expressivity of Inductive Logic Programming by Learning Different Kinds of Fuzzy Rules., and . Soft Comput., 11 (5): 459-466 (2007)Why Imprecise Regression: A Discussion., and . SMPS, volume 77 of Advances in Intelligent and Soft Computing, page 527-535. Springer, (2010)Learning First Order Fuzzy Logic Rules., , and . IFSA, volume 2715 of Lecture Notes in Computer Science, page 702-709. Springer, (2003)Possibilistic Inductive Logic Programming., and . ECSQARU, volume 3571 of Lecture Notes in Computer Science, page 675-686. Springer, (2005)Learning Disentangled Representations via Mutual Information Estimation., , and . CoRR, (2019)Possibilistic KNN Regression Using Tolerance Intervals., , and . IPMU (3), volume 299 of Communications in Computer and Information Science, page 410-419. Springer, (2012)Possibilistic classifiers for numerical data., , , and . Soft Comput., 17 (5): 733-751 (2013)An informational distance for estimating the faithfulness of a possibility distribution, viewed as a family of probability distributions, with respect to data., and . Int. J. Approx. Reason., 54 (7): 919-933 (2013)Elicitating Sugeno Integrals: Methodology and a Case Study., , , and . ECSQARU, volume 5590 of Lecture Notes in Computer Science, page 712-723. Springer, (2009)On the Induction of Different Kinds of First-Order Fuzzy Rules., , and . ECSQARU, volume 2711 of Lecture Notes in Computer Science, page 370-381. Springer, (2003)