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Titlebook: Rough Sets and Current Trends in Computing; 9th International Co Chris Cornelis,Marzena Kryszkiewicz,Lin Shang Conference proceedings 2014

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书目名称Rough Sets and Current Trends in Computing
副标题9th International Co
编辑Chris Cornelis,Marzena Kryszkiewicz,Lin Shang
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Rough Sets and Current Trends in Computing; 9th International Co Chris Cornelis,Marzena Kryszkiewicz,Lin Shang Conference proceedings 2014
描述This book constitutes the refereed proceedings of the 9th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2014, held in Granada and Madrid, Spain, in July 2014. RSCTC 2014 together with the Conference on Rough Sets and Emerging Intelligent Systems Paradigms (RSEISP 2014) was held as a major part of the 2014 Joint Rough Set Symposium (JRS 2014) The 23 regular and 17 short papers presented were carefully reviewed and selected from 120 submissions. They are organized in topical sections such as fuzzy logic and rough set: tools for imperfect information; fuzzy-rough hybridization; three way decisions and probabilistic rough sets; new trends in formal concept analysis and related methods; fuzzy decision making and consensus; soft computing for learning from data; web information systems and decision making; image processing and intelligent systems.
出版日期Conference proceedings 2014
关键词approximation; information retrieval; machine learning; perception; recommender systems; robustness; traff
版次1
doihttps://doi.org/10.1007/978-3-319-08644-6
isbn_softcover978-3-319-08643-9
isbn_ebook978-3-319-08644-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2014
The information of publication is updating

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Improving the ,-Precision and OWA Based Fuzzy Rough Set Models: Definitions, Properties and Robustne adjust the .-precision and the ordered weighted average based fuzzy rough set models in such a way that the number of theoretical properties increases. Furthermore, we evaluate the robustness of the new models a-.-PREC and a-OWA to noisy data and compare them to a general implicator-conjunctor-base
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Some Fundamental Laws of Partial First-Order Logic Based on Set Approximationsost general problem is what happens if in the semantics of first-order logic one uses the approximations of sets as semantic values of predicate parameters instead of sets given by their total interpretation in order to determine the truth values of formulas? The authors show some unexpected propert
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A Comparison of Two Versions of the MLEM2 Rule Induction Algorithm Extended to Probabilistic Approxiprobabilistic approximations were additionally generalized to an arbitrary binary relation so that probabilistic approximations may be applied for incomplete data. We discuss two ways to induce rules from incomplete data using probabilistic approximations, by applying true MLEM2 algorithm and an emu
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