國立虎尾科技大學 |

Learning from Data Streams in Evolving Environments = Methods and Applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Learning from Data Streams in Evolving Environments/ edited by Moamar Sayed-Mouchaweh.
Reminder of title:	Methods and Applications /
other author:	Sayed-Mouchaweh, Moamar.
Description:	VIII, 317 p. 131 illus., 95 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Electrical engineering. -
Online resource:	https://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. - 1st ed. 2019. - VIII, 317 p. 131 illus., 95 illus. in color.online resource. - Studies in Big Data,412197-6503 ;. - Studies in Big Data,8.

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:

596380
Electrical engineering.

LC Class. No.: TK1-9971

Dewey Class. No.: 621.382

Learning from Data Streams in Evolving Environments = Methods and Applications /
LDR:03382nam a22004095i 4500 001 1007136
003 DE-He213
005 20200703225715.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783319898032 $9 978-3-319-89803-2
024 7 $a 10.1007/978-3-319-89803-2 $2 doi
035 $a 978-3-319-89803-2
050 4 $a TK1-9971
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 621.382 $2 23
245 1 0 $a Learning from Data Streams in Evolving Environments $h [electronic resource] : $b Methods and Applications / $c edited by Moamar Sayed-Mouchaweh.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a VIII, 317 p. 131 illus., 95 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Big Data, $x 2197-6503 ; $v 41
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.
650 0 $a Electrical engineering. $3 596380
650 0 $a Quality control. $3 573723
650 0 $a Reliability. $3 573603
650 0 $a Industrial safety. $3 568114
650 0 $a Data mining. $3 528622
650 0 $a Control engineering. $3 1249728
650 1 4 $a Communications Engineering, Networks. $3 669809
650 2 4 $a Quality Control, Reliability, Safety and Risk. $3 671184
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Control and Systems Theory. $3 1211358
700 1 $a Sayed-Mouchaweh, Moamar. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 891963
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319898025
776 0 8 $i Printed edition: $z 9783319898049
776 0 8 $i Printed edition: $z 9783030078621
830 0 $a Studies in Big Data, $x 2197-6503 ; $v 8 $3 1256918
856 4 0 $u https://doi.org/10.1007/978-3-319-89803-2
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)