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This collection of articles is devoted to fuzzy as well as rough set theories. Both theoriesarebasedonrigorousideas, methodsandtechniquesinlogic, mathem- ics, and computer science for treating problems for which approximate solutions are possible only, due to their inherent ambiguity, vagueness, incompleteness, etc. Vast areas of decision making, data mining, knowledge discovery in data, approximatereasoning, etc., aresuccessfully exploredusing methods workedout within fuzzy and rough paradigms. By the very nature of fuzzy and rough paradigms, outlined above, they are related to distinct logical schemes: it is well-known that rough sets are related to modal logicsS5andS4(Orl owska, E., Modal logics in the theory of infor- tion systems, Z. Math. Logik Grund. Math. 30, 1984, pp. 213 ?.; Vakarelov, D., Modal logics for knowledgerepresentationsystems, LNCS 363,1989, pp. 257?.) and to ?nitely-valued logics (Pagliani, P., Rough set theory and logic-algebraic structures. In Incomplete Information: Rough Set Analysis, Orlo wska, E., ed., Physica/Springer, 1998, pp. 109 ?.; Polkowski, L. A note on 3-valued rough logic accepting decision rules, Fundamenta Informaticae 61, to appear). Fuzzy sets are related to in?nitely-valued logics (fuzzy membership to degree r? [0,1]expressingtruthdegreer)(Goguen, J.A., Thelogicofinexactconcepts, Synthese18/19,1968-9, pp.325?.;Pavelka, J., OnfuzzylogicI, II, III, Z. Math. Logik Grund. Math. 25, 1979, pp. 45 ?., pp. 119 ?., pp. 454 ?.; Dubois, D., Prade, H., Possibility Theory, Plenum Press, 1988; Haj ´ ek, P., Metamathematics of Fuzzy Logic, Kluwer, 1998).
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TRANSACTIONS ON ROUGH SETS II: ROUGH SETS AND FUZZY SETS
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Transactions on Rough Sets II: Rough Sets and Fuzzy Sets (Lecture Notes in Computer Science, 3135)
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Transactions on Rough Sets II
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Feature Selection with Rough Sets for Web Page Classification.- On Learning Similarity Relations in Fuzzy Case-Based Reasoning.- Incremental versus Non-incremental Rule Induction for Multicriteria Classification.- Three Strategies to Rule Induction from Dat. N° de réf. du vendeur 4886106
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Transactions on Rough Sets II : Rough Sets and Fuzzy Sets
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This collection of articles is devoted to fuzzy as well as rough set theories. Both theoriesarebasedonrigorousideas,methodsandtechniquesinlogic,mathem- ics, and computer science for treating problems for which approximate solutions are possible only, due to their inherent ambiguity, vagueness, incompleteness, etc. Vast areas of decision making, data mining, knowledge discovery in data, approximatereasoning,etc., aresuccessfully exploredusing methods workedout within fuzzy and rough paradigms. By the very nature of fuzzy and rough paradigms, outlined above, they are related to distinct logical schemes: it is well-known that rough sets are related to modal logicsS5andS4(Orl owska, E., Modal logics in the theory of infor- tion systems, Z. Math. Logik Grund. Math. 30, 1984, pp. 213 .; Vakarelov, D., Modal logics for knowledgerepresentationsystems,LNCS 363,1989,pp. 257 .) and to nitely-valued logics (Pagliani, P., Rough set theory and logic-algebraic structures. In Incomplete Information: Rough Set Analysis,Orlo wska, E., ed., Physica/Springer, 1998, pp. 109 .; Polkowski, L. A note on 3-valued rough logic accepting decision rules, Fundamenta Informaticae 61, to appear). Fuzzy sets are related to in nitely-valued logics (fuzzy membership to degree r [0,1]expressingtruthdegreer)(Goguen,J.A.,Thelogicofinexactconce pts, Synthese18/19,1968 9,pp.325 .;Pavelka,J.,OnfuzzylogicI,II,III,Z. Math. Logik Grund. Math. 25, 1979, pp. 45 ., pp. 119 ., pp. 454 .; Dubois, D., Prade, H., Possibility Theory, Plenum Press, 1988; Haj ek, P., Metamathematics of Fuzzy Logic, Kluwer, 1998). N° de réf. du vendeur 9783540239901
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Transactions on Rough Sets II
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ISBN 10 : 3540239901
ISBN 13 : 9783540239901
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This collection of articles is devoted to fuzzy as well as rough set theories. Both theoriesarebasedonrigorousideas,methodsandtechniquesinlogic,mathem- ics, and computer science for treating problems for which approximate solutions are possible only, due to their inherent ambiguity, vagueness, incompleteness, etc. Vast areas of decision making, data mining, knowledge discovery in data, approximatereasoning,etc., aresuccessfully exploredusing methods workedout within fuzzy and rough paradigms. By the very nature of fuzzy and rough paradigms, outlined above, they are related to distinct logical schemes: it is well-known that rough sets are related to modal logicsS5andS4(Orl owska, E., Modal logics in the theory of infor- tion systems, Z. Math. Logik Grund. Math. 30, 1984, pp. 213 .; Vakarelov, D., Modal logics for knowledgerepresentationsystems,LNCS 363,1989,pp. 257 .) and to nitely-valued logics (Pagliani, P., Rough set theory and logic-algebraic structures. In Incomplete Information: Rough Set Analysis,Orlo wska, E., ed., Physica/Springer, 1998, pp. 109 .; Polkowski, L. A note on 3-valued rough logic accepting decision rules, Fundamenta Informaticae 61, to appear). Fuzzy sets are related to in nitely-valued logics (fuzzy membership to degree r [0,1]expressingtruthdegreer)(Goguen,J.A.,Thelogicofinexactconcepts, Synthese18/19,1968 9,pp.325 .;Pavelka,J.,OnfuzzylogicI,II,III,Z. Math. Logik Grund. Math. 25, 1979, pp. 45 ., pp. 119 ., pp. 454 .; Dubois, D., Prade, H., Possibility Theory, Plenum Press, 1988; Haj ek, P., Metamathematics of Fuzzy Logic, Kluwer, 1998). 380 pp. Englisch. N° de réf. du vendeur 9783540239901
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