Produktbild: Learning from Data Streams in Evolving Environments
Band 41

Learning from Data Streams in Evolving Environments Methods and Applications

Aus der Reihe Studies in Big Data

97,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

14.12.2018

Abbildungen

VIII, 317 p. 131 illus., 95 illus. in color.

Herausgeber

Moamar Sayed-Mouchaweh

Verlag

Springer

Seitenzahl

317

Maße (L/B/H)

23,5/15,5/1,8 cm

Gewicht

499 g

Auflage

Softcover reprint of the original 1st ed. 2019

Sprache

Englisch

ISBN

978-3-030-07862-1

Beschreibung

Portrait


Moamar Sayed-Mouchaweh received his PhD from the University of Reims-France. He was working as Associated Professor in Computer Science, Control and Signal processing at the University of Reims-France in the Research centre in Sciences and Technology of the Information and the Communication. In December 2008, he obtained the Habilitation to Direct Research (HDR) in Computer science, Control and Signal processing. Since September 2011, he is working as a Full Professor in the High National Engineering School of Mines Telecom Lille Douai (France), Department of Computer Science and Automatic Control. He edited and wrote several Springer books and served as a guest editor of several special issues of international journals. He also served as IPC Chair and conference Chair of several international workshops and conferences. He is serving as a member of the Editorial Board of several international Journals.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

14.12.2018

Abbildungen

VIII, 317 p. 131 illus., 95 illus. in color.

Herausgeber

Moamar Sayed-Mouchaweh

Verlag

Springer

Seitenzahl

317

Maße (L/B/H)

23,5/15,5/1,8 cm

Gewicht

499 g

Auflage

Softcover reprint of the original 1st ed. 2019

Sprache

Englisch

ISBN

978-3-030-07862-1

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Learning from Data Streams in Evolving Environments

  • 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.