Download PDF by Przemyslaw Grzegorzewski, Marek Gagolewski, Olgierd: Strengthening Links Between Data Analysis and Soft Computing
By Przemyslaw Grzegorzewski, Marek Gagolewski, Olgierd Hryniewicz, María Ángeles Gil
This e-book gathers contributions provided on the seventh overseas convention on gentle tools in chance and information SMPS 2014, held in Warsaw (Poland) on September 22-24, 2014. Its objective is to give contemporary effects illustrating new traits in clever information research. It offers a accomplished evaluation of present study into the fusion of soppy computing equipment with likelihood and statistics.
Synergies of either fields may enhance clever info research tools by way of robustness to noise and applicability to bigger datasets, whereas having the ability to successfully receive comprehensible ideas of real-world problems.
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Additional info for Strengthening Links Between Data Analysis and Soft Computing
Grzegorzewski et al. 1007/978-3-319-10765-3_3 22 I. Montes, E. Miranda, and S. Montes some additional remarks and a discussion of other approaches to this problem. Proofs are omitted because of space limitations. 1 Preliminary Notions Fuzzy Random Variables Fuzzy random variables were introduced simultaneously by Kruse and Meyer  and Puri and Ralescu . In this paper, we follow the epistemic approach considered in . Let F (R) denote the set of all fuzzy sets on R. Deﬁnition 1 (). A fuzzy random variable is a map X : Ω → F (R) such that the α-cuts Xα are strongly measurable multi-valued mappings.
These works are in preparation. References 1. : Ambiguity of Fuzzy Quantities and a New Proposal for their Ranking. Przeglad Elektrotechniczny-Electrical Review 10, 280–283 (2012) 2. : The total variation of bounded variation functions to evaluate and rank fuzzy quantities. International Journal of Intelligent Systems 28, 927–956 (2013) 3. : Evaluation and interval approximation of fuzzy quantities. In: Proceedings of 8th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2013), pp.
Applying the results concerning normalization of interval weights that were proved in , we ﬁnd out that the following general relations hold: 34 O. Pavlaˇcka and K. Hron Table 3. 253 j, k = 1, . . , D, j = k. Example 1. 932] (see Table 2). 125] . 125]. The interactions among the proportions [cik , cik ], k = 1, . . , D, mean that the proper proportional representation of interval compositional data Xi , i = 1, . . , n, has to be given in the following way: C(Xi ) := C(xi ) ∈ [0, 1]D | xi ∈ [xi1 , xi1 ] × .
Strengthening Links Between Data Analysis and Soft Computing by Przemyslaw Grzegorzewski, Marek Gagolewski, Olgierd Hryniewicz, María Ángeles Gil