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Integrating new classes on the fly in evolving fuzzy classifier designs and their application in visual inspection.

, , , , and . Appl. Soft Comput., (2015)

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Separated Antecedent and Consequent Learning for Takagi-Sugeno Fuzzy Systems., , , , and . FUZZ-IEEE, page 2263-2269. IEEE, (2006)Approximation of incoherent probabilities., and . Int. J. Approx. Reason., (2019)Editorial of the special issue: Online fuzzy machine learning and data mining., , and . Inf. Sci., (2013)Modeling and control with neural networks for a magnetic levitation system., , , , , and . Neurocomputing, (2017)Editorial on the special issue "Hybrid and ensemble techniques in soft computing: recent advances and emerging trends"., , and . Soft Comput., 19 (12): 3353-3355 (2015)Data-driven evolving fuzzy systems using eTS and FLEXFIS: comparative analysis., and . Int. J. General Systems, 37 (1): 45-67 (2008)Machine learning based analysis of gender differences in visual inspection decision making., , , , and . Inf. Sci., (2013)DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm., , , , and . Soft Comput., 19 (12): 3551-3569 (2015)DEVDAN: Deep evolving denoising autoencoder., , , and . Neurocomputing, (2020)Reliable All-Pairs Evolving Fuzzy Classifiers., and . IEEE Trans. Fuzzy Syst., 21 (4): 625-641 (2013)