The Resource Uncertainty modeling for data mining : a label semantics approach, Zengchang Qin, Yongchuan Tang, (electronic book)
Uncertainty modeling for data mining : a label semantics approach, Zengchang Qin, Yongchuan Tang, (electronic book)
Resource Information
The item Uncertainty modeling for data mining : a label semantics approach, Zengchang Qin, Yongchuan Tang, (electronic book) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item Uncertainty modeling for data mining : a label semantics approach, Zengchang Qin, Yongchuan Tang, (electronic book) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.
This item is available to borrow from 1 library branch.
- Summary
- 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
- Language
- eng
- Label
- Uncertainty modeling for data mining : a label semantics approach
- Title
- Uncertainty modeling for data mining
- Title remainder
- a label semantics approach
- Statement of responsibility
- Zengchang Qin, Yongchuan Tang
- Language
- eng
- Summary
- 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
- Cataloging source
- N$T
- http://library.link/vocab/creatorName
- Qin, Zengchang
- Dewey number
- 003/.54
- Illustrations
- illustrations
- Index
- no index present
- LC call number
- Q375
- Literary form
- non fiction
- Nature of contents
-
- dictionaries
- bibliography
- http://library.link/vocab/relatedWorkOrContributorName
- Tang, Yongchuan
- Series statement
- Advanced Topics in Science and Technology in China,
- http://library.link/vocab/subjectName
-
- Uncertainty (Information theory)
- Data mining
- SCIENCE / System Theory
- TECHNOLOGY & ENGINEERING / Operations Research
- Label
- Uncertainty modeling for data mining : a label semantics approach, Zengchang Qin, Yongchuan Tang, (electronic book)
- Antecedent source
- unknown
- Bibliography note
- Includes bibliographical references
- Color
- multicolored
- Control code
- SPR894509487
- Dimensions
- unknown
- Extent
- 1 online resource (420 pages)
- File format
- unknown
- Form of item
- online
- Isbn
- 9783642412516
- Level of compression
- unknown
- Other control number
- 10.1007/978-3-642-41251-6
- Other physical details
- illustrations.
- Quality assurance targets
- not applicable
- Reformatting quality
- unknown
- Reproduction note
- Electronic resource.
- Sound
- unknown sound
- Specific material designation
- remote
- Label
- Uncertainty modeling for data mining : a label semantics approach, Zengchang Qin, Yongchuan Tang, (electronic book)
- Antecedent source
- unknown
- Bibliography note
- Includes bibliographical references
- Color
- multicolored
- Control code
- SPR894509487
- Dimensions
- unknown
- Extent
- 1 online resource (420 pages)
- File format
- unknown
- Form of item
- online
- Isbn
- 9783642412516
- Level of compression
- unknown
- Other control number
- 10.1007/978-3-642-41251-6
- Other physical details
- illustrations.
- Quality assurance targets
- not applicable
- Reformatting quality
- unknown
- Reproduction note
- Electronic resource.
- Sound
- unknown sound
- Specific material designation
- remote
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.liverpool.ac.uk/portal/Uncertainty-modeling-for-data-mining--a-label/tR7zJDeM9rg/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.liverpool.ac.uk/portal/Uncertainty-modeling-for-data-mining--a-label/tR7zJDeM9rg/">Uncertainty modeling for data mining : a label semantics approach, Zengchang Qin, Yongchuan Tang, (electronic book)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.liverpool.ac.uk/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.liverpool.ac.uk/">Sydney Jones Library, University of Liverpool</a></span></span></span></span></div>
