Soft Computing for Information Processing and Analysis by Nikravesh M. (Ed), Kacprzyk J. (Ed), Zadeh L. A. (Ed)

By Nikravesh M. (Ed), Kacprzyk J. (Ed), Zadeh L. A. (Ed)

"Soft Computing for info Processing and research" contains reviews from front of sentimental computing within the web and imparts wisdom and figuring out of the importance of the fields accomplishments, new advancements and destiny instructions.

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By Nikravesh M. (Ed), Kacprzyk J. (Ed), Zadeh L. A. (Ed)

"Soft Computing for info Processing and research" contains reviews from front of sentimental computing within the web and imparts wisdom and figuring out of the importance of the fields accomplishments, new advancements and destiny instructions.

Show description

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Proceedings of the AAAI Workshop on Knowledge Discovery in Databases, pp. 181 - 192, Seattle, USA, 1994. G. Raschia and N. Mouaddib. SAINTETIQ: a fuzzy set-based approach to data­ base summarization. Fuzzy Sets and Systems, 129, 137 - 162, 2002. D. R. Yager. Fuzzy query language for hypothesis evalua­ tion. , H. Christiansen and H. L. ): Flexible Query Answering Systems, pp. 23 - 43, Kluwer, Boston, 1997. D. R. Yager. edu ^^^ Dept. of Computer Science, Meiji University. ^^^National Institute of Informatics Abstract: Retrieving relevant information is a crucial component of cased-based reasoning systems for Internet applications such as search engines.

Prade. Beyond min aggregation in multicriteria decision: (ordered) weighted min, discri-min,leximin. R. Yager and J. ): The Ordered Weighted Averaging Operators. Theory and Ap­ plications, pp. 181 - 192, Kluwer, Boston, 1997. 6. D. Dubois and H. Prade. Gradual rules in approximate reasoning. Information Sciences, 61, 103 - 122, 1992. 7. D. Dubois and H. Prade. Fuzzy sets in approximate reasoning. Part 1: Inference with possibility distributions. Fuzzy Sets and Systems, 40, 143 - 202, 1991. 8. P.

In their approach, the fitting function is a combination of a wide array of indices: a degree of imprecision (fuzziness), a degree of covering, a degree of appropri­ ateness, a length of a summary, etc. (cf. also Kacprzyk and Yager [16]). Rasmussen and Yager [34, 35] propose an extension, SummarySQL, to SQL to cover linguistic summaries. Actually, they do not address the mining linguistic summaries but merely their verification. The SummarySQL may also be used to verify a kind of fuzzy gradual rules (cf.

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