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 publication constitutes the refereed complaints of the thirteenth foreign convention on details Processing and administration of Uncertainty in Knowledge-Based structures, 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 analysis within the box of Computational Intelligence and signify advancements on subject matters as: computer studying, facts mining, trend acceptance, uncertainty dealing with, aggregation and fusion of data in addition to common sense and data processing.

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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

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3. 4. 17 0 ≤ Sim(a, b) ≤ 1. Sim(a, b) = 1 if a = b. Sim(a, b) is increasing in Si (a, b). Transitivity: Sim(a, b) ≤ min{Sim(a, c), Sim(c, b)}. However, defining Sim(a, b) by equation (6) has also drawbacks: since Si ∈ [0, 1], Sim ≤ Si and in fact, it may become very small (for practical purposes 0) as the number of dimensions increases. A common device to avoid this is to consider n n − ln( i=1 Si ) = − i=1 ln(Si ) (the log-likelihood), where the minus sign is used to obtain a result greater than zero.

SIAM J. Appl. Math. 37, 148–165 (1979) 2. : Indice de similarit´e et pr´eordonnance associ´ee. In: S´eminaire sur les ordres totaux finis, Aix-en-Provence, pp. 233–243 (1967) 3. : A classification of presence/absence based dissimilarity coefficients. Journal of Classification 6, 233–246 (1989) 4. : Comparing resemblance measures. Journal of Classification 12, 73–90 (1995) 5. : Ranking invariance based on similarity measures in document retrieval. K. ) AMR 2005. LNCS, vol. 3877, pp. 55–64. Springer, Heidelberg (2006) 6.

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. For example, one can use algorithms such as the EM algorithm for approximating the real distribution by mixtures of Gaussian distributions, and then proceed as indicated above, or (2) one can compute directly a histogram distances, of the values d(xi , xj ), for values xi of the attribute X.

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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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