Learning from Data Streams in Evolving Environments : Methods and Applications / edited by Moamar Sayed-Mouchaweh

Mitwirkende(r):

Sayed-Mouchaweh, Moamar [HerausgeberIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Studies in Big Data ; 41 | SpringerLink BücherVerlag: Cham : Springer International Publishing, 2019Beschreibung: Online-Ressource (VIII, 317 p. 131 illus., 95 illus. in color, online resource)ISBN:

9783319898032

Schlagwörter:

Andere physische Formen: 9783319898025 | Erscheint auch als: 978-3-319-89802-5 Druck-Ausgabe | Printed edition: 9783319898025 DDC-Klassifikation:

621.382

LOC-Klassifikation:

TK1-9971

DOI: DOI: 10.1007/978-3-319-89803-2Online-Ressourcen:

Zusammenfassung: 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 directionsZusammenfassung: 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 ClusteringPPN: PPN: 1028032668Package identifier: Produktsigel: ZDB-2-ENG | ZDB-2-SEB | ZDB-2-SXE

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