Stock image for illustration purposes only - book cover, edition or condition may vary.
Uncertainty Modeling for Data Mining: A Label Semantics Approach (Advanced Topics in Science and Technology in China)
Zengchang Qin
€ 126.37
FREE Delivery in Ireland
Description for Uncertainty Modeling for Data Mining: A Label Semantics Approach (Advanced Topics in Science and Technology in China)
Hardcover. Outlining a new research direction in fuzzy set theory applied to data mining, this volume proposes a number of new data mining algorithms and includes dozens of figures and illustrations that help the reader grasp the complexities of the concepts. Series: Advanced Topics in Science and Technology in China. Num Pages: 291 pages, biography. BIC Classification: UNF; UYAM; UYM; UYQL. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 23. Weight in Grams: 617.
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 ... Read more
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 ... Read more
Product Details
Publisher
Springer
Format
Hardback
Publication date
2015
Series
Advanced Topics in Science and Technology in China
Condition
New
Weight
604g
Number of Pages
291
Place of Publication
Berlin, Germany
ISBN
9783642412509
SKU
V9783642412509
Shipping Time
Usually ships in 5 to 9 working days
Ref
99-8
Reviews for Uncertainty Modeling for Data Mining: A Label Semantics Approach (Advanced Topics in Science and Technology in China)