國立虎尾科技大學 |

Natural Computing for Unsupervised Learning

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Natural Computing for Unsupervised Learning/ edited by Xiangtao Li, Ka-Chun Wong.
其他作者:	Li, Xiangtao.
面頁冊數:	VI, 273 p. 121 illus., 79 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Electrical engineering. -
電子資源:	https://doi.org/10.1007/978-3-319-98566-4
ISBN:	9783319985664

Natural Computing for Unsupervised Learning
Natural Computing for Unsupervised Learning[electronic resource] /edited by Xiangtao Li, Ka-Chun Wong. - 1st ed. 2019. - VI, 273 p. 121 illus., 79 illus. in color.online resource. - Unsupervised and Semi-Supervised Learning,2522-848X. - Unsupervised and Semi-Supervised Learning,.

Introduction -- Part I – Basic Natural Computing Techniques for Unsupervised Learning -- Hard Clustering using Evolutionary Algorithms -- Soft Clustering using Evolutionary Algorithms -- Fuzzy / Rough Set Systems for Unsupervised Learning -- Unsupervised Feature Selection using Evolutionary Algorithms -- Unsupervised Feature Selection using Artificial Neural Networks -- Part II – Advanced Natural Computing Techniques for Unsupervised Learning -- Hybrid Genetic Algorithms for Feature Subset Selection in Model-Based Clustering -- Nature-Inspired Optimization Approaches for Unsupervised Feature Selection -- Co-Evolutionary Approaches for Unsupervised Learning -- Mining Evolving Patterns using Natural Computing Techniques -- Multi-objective Optimization for Unsupervised Learning -- Many-objective Optimization for Unsupervised Learning -- Part III – Applications -- Unsupervised Identification of DNA-binding Proteins using Natural Computing Techniques -- Parallel Solution-based Natural Clustering Techniques on Railway Engineering data -- Natural Computing Techniques for Community Detection on Online Social Networks -- Big Data Challenges and Scalability in Natural Computing for Unsupervised Learning -- Conclusion.

This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes information on the use of natural computing techniques for unsupervised learning tasks. It features several trending topics, such as big data scalability, wireless network analysis, engineering optimization, social media, and complex network analytics. It shows how these applications have triggered a number of new natural computing techniques to improve the performance of unsupervised learning methods. With this book, the readers can easily capture new advances in this area with systematic understanding of the scope in depth. Readers can rapidly explore new methods and new applications at the junction between natural computing and unsupervised learning. Includes advances on unsupervised learning using natural computing techniques Reports on topics in emerging areas such as evolutionary multi-objective unsupervised learning Features natural computing techniques such as evolutionary multi-objective algorithms and many-objective swarm intelligence algorithms.

ISBN: 9783319985664

Standard No.: 10.1007/978-3-319-98566-4doiSubjects--Topical Terms:

596380
Electrical engineering.

LC Class. No.: TK1-9971

Dewey Class. No.: 621.382

Natural Computing for Unsupervised Learning
LDR:03912nam a22004095i 4500 001 1013616
003 DE-He213
005 20200702205738.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783319985664 $9 978-3-319-98566-4
024 7 $a 10.1007/978-3-319-98566-4 $2 doi
035 $a 978-3-319-98566-4
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 Natural Computing for Unsupervised Learning $h [electronic resource] / $c edited by Xiangtao Li, Ka-Chun Wong.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a VI, 273 p. 121 illus., 79 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 Unsupervised and Semi-Supervised Learning, $x 2522-848X
505 0 $a Introduction -- Part I – Basic Natural Computing Techniques for Unsupervised Learning -- Hard Clustering using Evolutionary Algorithms -- Soft Clustering using Evolutionary Algorithms -- Fuzzy / Rough Set Systems for Unsupervised Learning -- Unsupervised Feature Selection using Evolutionary Algorithms -- Unsupervised Feature Selection using Artificial Neural Networks -- Part II – Advanced Natural Computing Techniques for Unsupervised Learning -- Hybrid Genetic Algorithms for Feature Subset Selection in Model-Based Clustering -- Nature-Inspired Optimization Approaches for Unsupervised Feature Selection -- Co-Evolutionary Approaches for Unsupervised Learning -- Mining Evolving Patterns using Natural Computing Techniques -- Multi-objective Optimization for Unsupervised Learning -- Many-objective Optimization for Unsupervised Learning -- Part III – Applications -- Unsupervised Identification of DNA-binding Proteins using Natural Computing Techniques -- Parallel Solution-based Natural Clustering Techniques on Railway Engineering data -- Natural Computing Techniques for Community Detection on Online Social Networks -- Big Data Challenges and Scalability in Natural Computing for Unsupervised Learning -- Conclusion.
520 $a This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes information on the use of natural computing techniques for unsupervised learning tasks. It features several trending topics, such as big data scalability, wireless network analysis, engineering optimization, social media, and complex network analytics. It shows how these applications have triggered a number of new natural computing techniques to improve the performance of unsupervised learning methods. With this book, the readers can easily capture new advances in this area with systematic understanding of the scope in depth. Readers can rapidly explore new methods and new applications at the junction between natural computing and unsupervised learning. Includes advances on unsupervised learning using natural computing techniques Reports on topics in emerging areas such as evolutionary multi-objective unsupervised learning Features natural computing techniques such as evolutionary multi-objective algorithms and many-objective swarm intelligence algorithms.
650 0 $a Electrical engineering. $3 596380
650 0 $a Signal processing. $3 561459
650 0 $a Image processing. $3 557495
650 0 $a Speech processing systems. $3 564428
650 0 $a Pattern recognition. $3 1253525
650 0 $a Artificial intelligence. $3 559380
650 0 $a Data mining. $3 528622
650 1 4 $a Communications Engineering, Networks. $3 669809
650 2 4 $a Signal, Image and Speech Processing. $3 670837
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
700 1 $a Li, Xiangtao. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1307877
700 1 $a Wong, Ka-Chun. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1114496
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319985657
776 0 8 $i Printed edition: $z 9783319985671
776 0 8 $i Printed edition: $z 9783030075088
830 0 $a Unsupervised and Semi-Supervised Learning, $x 2522-848X $3 1304411
856 4 0 $u https://doi.org/10.1007/978-3-319-98566-4
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)