【2015新书】Uncertainty Modeling for Data Mining: A Label Semantics Approach

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图书名称：Uncertainty Modeling for Data Mining: A Label Semantics Approach
作者：Zengchang Qin, Yongchuan Tang
出版社：Springer
页数：420
出版时间：2015                           
语言：English
格式：pdf
内容简介：
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.



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关键词：uncertainty Data Mining Uncertain certainty Semantic connected learning English usually 出版社

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