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Learning from data streams in evolvi...

Sayed-Mouchaweh, Moamar.

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Learning from data streams in evolving environments = methods and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Learning from data streams in evolving environments/ edited by Moamar Sayed-Mouchaweh.
其他題名:	methods and applications /
其他作者:	Sayed-Mouchaweh, Moamar.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	viii, 317 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Machine learning. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-89803-2
ISBN:	9783319898032

Learning from data streams in evolving environments = methods and applications /
Learning from data streams in evolving environmentsmethods and applications /[electronic resource] :edited by Moamar Sayed-Mouchaweh. - Cham :Springer International Publishing :2019. - viii, 317 p. :ill., digital ;24 cm. - Studies in big data,v.412197-6503 ;. - Studies in big data ;v.41..

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.

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.

ISBN: 9783319898032

Standard No.: 10.1007/978-3-319-89803-2doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Learning from data streams in evolving environments = methods and applications /
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505 0 $a 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.
520 $a 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.
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