Natural Computing for Unsupervised Learning (Record no. 77024)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04780nam a22006015i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-319-98566-4 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220801215025.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 181031s2019 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783319985664 |
| -- | 978-3-319-98566-4 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 621.382 |
| 245 10 - TITLE STATEMENT | |
| Title | Natural Computing for Unsupervised Learning |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2019. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | VI, 273 p. 121 illus., 79 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Unsupervised and Semi-Supervised Learning, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Li, Xiangtao. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Wong, Ka-Chun. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-319-98566-4 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2019. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Telecommunication. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Signal processing. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Pattern recognition systems. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data mining. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Communications Engineering, Networks. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Signal, Speech and Image Processing . |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Automated Pattern Recognition. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Mining and Knowledge Discovery. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2522-8498 |
| 912 ## - | |
| -- | ZDB-2-ENG |
| 912 ## - | |
| -- | ZDB-2-SXE |
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