Granular Computing Based Machine Learning (Record no. 79717)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03894nam a22005415i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-319-70058-8 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220801221459.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 171104s2018 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783319700588 |
| -- | 978-3-319-70058-8 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Liu, Han. |
| 245 10 - TITLE STATEMENT | |
| Title | Granular Computing Based Machine Learning |
| Sub Title | A Big Data Processing Approach / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2018. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XV, 113 p. 27 illus., 19 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Studies in Big Data, |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs—Volume, Velocity, Variety, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data. Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries. This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Cocea, Mihaela. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-319-70058-8 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2018. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Big data. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Quantitative research. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Big Data. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Analysis and Big Data. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2197-6511 ; |
| 912 ## - | |
| -- | ZDB-2-ENG |
| 912 ## - | |
| -- | ZDB-2-SXE |
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