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Titlebook: Rough Sets and Intelligent Systems Paradigms; International Confer Marzena Kryszkiewicz,James F. Peters,Andrzej Skowr Conference proceeding

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發(fā)表于 2025-3-21 16:51:16 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Rough Sets and Intelligent Systems Paradigms
副標(biāo)題International Confer
編輯Marzena Kryszkiewicz,James F. Peters,Andrzej Skowr
視頻videohttp://file.papertrans.cn/832/831913/831913.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Rough Sets and Intelligent Systems Paradigms; International Confer Marzena Kryszkiewicz,James F. Peters,Andrzej Skowr Conference proceeding
出版日期Conference proceedings 2007
關(guān)鍵詞Spam; adaptive learning; algorithmic learning; approximation; association mining; attribute reducts; bioin
版次1
doihttps://doi.org/10.1007/978-3-540-73451-2
isbn_softcover978-3-540-73450-5
isbn_ebook978-3-540-73451-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2007
The information of publication is updating

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Granular Computing and Rough Set Theory whose values are labeled with words drawn from a natural language. For example, if . is temperature, then 101.3 is a singular value of temperature, while ”high” is a granular (linguistic) value of temperature.
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Rough Sets and Vague SetsThe considerations intend to take into account a classical approach to reasoning, based on vague premises, and suggest finding a logic of vague sentences as a non-classical logic in which all counterparts of tautologies of classical logic are laws.
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發(fā)表于 2025-3-22 13:12:05 | 只看該作者
Certain, Generalized Decision, and Membership Distribution Reducts Versus Functional Dependencies inve that, unlike in the case of complete systems, the reducts in incomplete systems are not guaranteed to be minimal sets of conditional attributes that functionally determine respective modifications of the decision attribute.
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Dependencies in Structures of Decision Tablesion of the Pawlak’s measure of the dependency between attributes and the expected certainty gain measure. The expected certainty gain measure reflects the subtle grades of probabilistic dependence of events. The measures are reviewed and it is shown how they can be extended to dependencies existing in hierarchical structures of decision tables.
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Dominance-Based Rough Set Approach to Reasoning About Ordinal Datauniverse, and about monotonic relationships between these evaluations, e.g. “the larger the mass and the smaller the distance, the larger the gravity” or “the greater the debt of a firm, the greater its risk of failure”. Such a knowledge is typical for data describing various phenomena, and for data
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