Serrurier Marseille E-books > International > Download e-book for iPad: Computational Intelligence for Knowledge-Based Systems by Bernadette Bouchon-Meunier, Giulianella Coletti,

Download e-book for iPad: Computational Intelligence for Knowledge-Based Systems by Bernadette Bouchon-Meunier, Giulianella Coletti,

By Bernadette Bouchon-Meunier, Giulianella Coletti, Marie-Jeanne Lesot, Maria Rifqi (auth.), Eyke Hüllermeier, Rudolf Kruse, Frank Hoffmann (eds.)

ISBN-10: 3642140483

ISBN-13: 9783642140488

ISBN-10: 3642140491

ISBN-13: 9783642140495

The e-book constitutes the refereed lawsuits of the thirteenth overseas convention on info Processing and administration of Uncertainty in Knowledge-Based platforms, IPMU 2010, held in Dortmund, Germany from June 28 - July 2, 2010. The seventy seven revised complete papers have been conscientiously reviewed and chosen from 320 submissions and mirror the richness of study within the box of Computational Intelligence and characterize advancements on themes as: computing device studying, information mining, trend popularity, uncertainty dealing with, aggregation and fusion of knowledge in addition to good judgment and data processing.

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Read or Download Computational Intelligence for Knowledge-Based Systems Design: 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings PDF

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Extra resources for Computational Intelligence for Knowledge-Based Systems Design: 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings

Example text

XMin the overall similarity is defined as the tail probability corresponding to the 1st order statistic of the exponential distribution with λ = 1/2: 2 ) Simm,χ2 (a, b) = P (χ22 ≥ Xm (9) 18 4 A. Ralescu, S. Visa, and S. Popovici Concluding Remarks and Future Work In the above discussion we made the assumption that the data comes from a known distribution. However, in many applications this may not be the case. In such cases, we can adopt one of the following procedures: (1) Use the available data to obtain an estimate of the underlying distribution.

C˜c˜. The set similarity of two subsets Ci and C˜j , i ∈ {1, . . , c}, j ∈ {1, . . , c˜} is defined as Ci ∩ C˜j sij = (21) Ci ∪ C˜j If Ci and C˜j are fuzzy sets, then the set similarity is defined as n sij = k=1 n k=1 T (uik , u ˜jk ) (22) C(uik , u ˜jk ) where T is a suitable T norm and C is a suitable T conorm [10]. In this paper, we choose T = min and C = max, which leads to n sij = k=1 n k=1 min(uik , u ˜jk ) (23) max(uik , u ˜jk ) i = 1, . . , c, j = 1, . . , c˜. The denominator becomes zero if uik = u˜jk = 0 for all k = 1, .

Cc = C˜1 ) ∨ (Cc = C˜2 ) . . ∨ (Cc = C˜c˜) (24) ˜ if and only The partition superset operator can be defined accordingly: U ⊇ U if (C1 = C˜1 ) ∨ (C2 = C˜1 ) . . ∨ (Cc = C˜1 ) ∧ (C1 = C˜2 ) ∨ (C2 = C˜2 ) . . ∨ (Cc = C˜2 ) (25) .. . ∧ (C1 = C˜c˜) ∨ (C2 = C˜c˜) . . ∨ (Cc = C˜c˜) ˜ are similar if and only if U ⊆ U ˜ or U ⊇ U ˜ . For fuzzy We define that U and U partitions, we define the subset measure ˜ ) = T C(s11 , s12 , . . , s1˜c ), S⊆ (U, U C(s21 , s22 , . . , s2˜c ), .. C(sc1 , sc2 , .

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Computational Intelligence for Knowledge-Based Systems Design: 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings by Bernadette Bouchon-Meunier, Giulianella Coletti, Marie-Jeanne Lesot, Maria Rifqi (auth.), Eyke Hüllermeier, Rudolf Kruse, Frank Hoffmann (eds.)


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