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Uncertainty modeling for data mining [electronic resource] : a label semantics approach

Uncertainty modeling for data mining [electronic resource] : a label semantics approach

자료유형
E-Book(소장)
개인저자
Qin, Zengchang. Tang, Yongchuan.
서명 / 저자사항
Uncertainty modeling for data mining [electronic resource] : a label semantics approach / Zengchang Qin, Yongchuan Tang.
발행사항
Berlin;   Heidelberg :   Springer Berlin Heidelberg :   Imprint: Springer,   2014.  
형태사항
1 online resource (xix, 291 p.).
총서사항
Advanced topics in science and technology in China,1995-6819
ISBN
9783642412516
요약
Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning.   Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.
일반주기
Title from e-Book title page.  
서지주기
Includes bibliographical references.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Uncertainty (Information theory) . Data mining.
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245 1 0 ▼a Uncertainty modeling for data mining ▼h [electronic resource] : ▼b a label semantics approach / ▼c Zengchang Qin, Yongchuan Tang.
260 ▼a Berlin; ▼a Heidelberg : ▼b Springer Berlin Heidelberg : ▼b Imprint: Springer, ▼c 2014.
300 ▼a 1 online resource (xix, 291 p.).
490 1 ▼a Advanced topics in science and technology in China, ▼x 1995-6819
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references.
520 ▼a Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning.   Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Uncertainty (Information theory) .
650 0 ▼a Data mining.
700 1 ▼a Tang, Yongchuan.
830 0 ▼a Advanced topics in science and technology in China.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-642-41251-6
945 ▼a KLPA
991 ▼a E-Book(소장)

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