Results 281 to 290 of about 100,287 (312)
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2016
This paper presents a new mereological approach to formalizing geometric notions of incidence, congruence, and parallelism over extended regions. The axiomatization was built extending a decidable pre-mereological base language, showing where the geometric framework requires first-order extension.
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This paper presents a new mereological approach to formalizing geometric notions of incidence, congruence, and parallelism over extended regions. The axiomatization was built extending a decidable pre-mereological base language, showing where the geometric framework requires first-order extension.
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Concept Granular System and Granular Concept Lattice
2008 The 9th International Conference for Young Computer Scientists, 2008This paper, based on the authorpsilas new model of granular computing--the theory of granular set, puts forward the concept of granular family, granular system, concept granular system, granular concept, through upgrading the mapping to the power set and mapping from one-way to two-way.
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Information Granularity and Granular Structure in Decision Making
2012Multiple criteria decision making (MCDM) has received increasing attentions in both engineering and economic fields. Weights of the criteria directly affect decision results in MCDM, so it is important for us to acquire the appropriate weights of the criteria.
Baoli Wang, Jiye Liang, Yuhua Qian
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Granular Networks and Granular Learning
2002The study is concerned with the fundamentals of granular computing and its application to neural networks. Granular computing, as the name itself stipulates, deals with representing information in the form of some aggregates (embracing a number of individual entitites) and their ensuing processing.
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Mutual implications and granularity
Knowledge Acquisition, 1992Abstract This paper illustrates a technique for discovering mutual implications among hierarchically structured data. Such a technique may be applied to both knowledge and data bases. If the hierarchical structure makes it possible to define granularity levels, mutual implications can be evaluated at any level. Results can be quantitative (i.e.
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Chaos: An Interdisciplinary Journal of Nonlinear Science, 2005
E, Ben-Naim, Z A, Daya, R E, Ecke
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E, Ben-Naim, Z A, Daya, R E, Ecke
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Multi-level granularity entropies for fuzzy coverings and feature subset selection
Artificial Intelligence Review, 2023Zhehuang Huang, Jinjin Li, Li Jinjin
exaly
Tri-granularity attribute reduction of three-way concept lattices
Knowledge-Based Systems, 2023Zhen Wang, Chengjun Shi, Ling Wei
exaly

