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Learning from Data Streams in Evolving Environments:Methods and Applications (Studies in Big Data, Vol. 41) '18

Sayed-Mouchaweh, Moamar 　編

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価格 \21,939（税込）

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発行年月	2018年08月
出版社／提供元	Springer International Publishing
出版国	スイス
言語	英語
媒体	冊子
装丁	hardcover
ページ数／巻数	VIII, 317 p. 131 illus., 95 illus. in color.
ジャンル	洋書／理工学／電気電子工学／通信・電波工学
ISBN	9783319898025
商品コード	1027255333
本の性格	学術書
新刊案内掲載月	2018年06月
商品URL	https://kw.maruzen.co.jp/ims/itemDetail.html?itmCd=1027255333

内容

This edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by evolving non-stationary processes. The goal is to discuss and overview the advanced techniques, methods and tools that are dedicated to manage, exploit and interpret data streams in non-stationary environments. The book includes the required notions, definitions, and background to understand the problem of learning from data streams in non-stationary environments and synthesizes the state-of-the-art in the domain, discussing advanced aspects and concepts and presenting open problems and future challenges in this field.
Provides multiple examples to facilitate the understanding data streams in non-stationary environments; Presents several application cases to show how the methods solve different real world problems; Discusses the links between methods to help stimulate new research and application directions.

Chapter1: Transfer Learning in Non-Stationary Environments.- Chapter2: A new combination of diversity techniques in ensemble classifiers for handling complex concept drift.- Chapter3: Analyzing and Clustering Pareto-Optimal Objects in Data Streams.- Chapter4: Error-bounded Approximation of Data Stream: Methods and Theories.- Chapter5: Ensemble Dynamics in Non-stationary Data Stream Classification.- Chapter6: Processing Evolving Social Networks for Change Detection based on Centrality Measures.- Chapter7: Large-scale Learning from Data Streams with Apache SAMOA.- Chapter8: Process Mining for Analyzing Customer Relationship Management Systems A Case Study.- Chapter9: Detecting Smooth Cluster Changes in Evolving Graph Sequences.- Chapter10: Efficient Estimation of Dynamic Density Functions with Applications in Data Streams.- Chapter11: A Survey of Methods of Incremental Support Vector Machine Learning.- Chapter12: On Social Network-based Algorithms for Data Stream Clustering.

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