Titlebook: Learning Disabilities and Brain Function; A Neuropsychological William H. Gaddes Book 19852nd edition Springer-Verlag New York 1985 Brain.F

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William H. Gaddesthis research shows that the adaptation with NPI-M has an equivalent performance than the one obtained with the adaptation based on the IDM with the best choice of the hyperparameter. Consequently, since the NPI-M is a non-parametric approach, it is concluded that the NPI-M is more appropriated than

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William H. Gaddeses in artificial intelligence; belief function theory and its applications; aggregation: theory and practice; aggregation: pre-aggregation functions and other generalizations of monotonicity; aggregation: aggregation of different data structures; fuzzy methods in data mining and knowledge discovery;

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William H. Gaddesthis research shows that the adaptation with NPI-M has an equivalent performance than the one obtained with the adaptation based on the IDM with the best choice of the hyperparameter. Consequently, since the NPI-M is a non-parametric approach, it is concluded that the NPI-M is more appropriated than

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William H. Gaddesent it into a categorical-sequential clustering algorithm by combining it with sequential alignment. Finally, we treat each resulting cluster by building individual Markov models of different orders, expecting that the representative characteristics of each cluster are captured.

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William H. Gaddesther characteristics leads to the selection of the same uniform distribution: e.g., estimating the largest possible values of generalized entropy or of some sensitivity-related characteristics. In this paper, we provide a general explanation of why uniform distribution appears in different situation

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ber of labels needed. Next, sample informativeness can be exploited in teacher-based algorithms to additionally weigh data by certainty. In addition, multi-target learning of different labeller tracks in parallel and/or of the uncertainty can help improve the model robustness and provide an addition

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William H. Gaddescertainty in Knowledge-Based Systems, IPMU 2016, held in Eindhoven, The Netherlands, in June 2016...The 127 revised full papers presented together with four invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on fuzzy measures an