Proceedings of ELM-2016 (Record no. 80006)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04073nam a22005535i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-319-57421-9 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220801221738.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 170525s2018 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783319574219 |
| -- | 978-3-319-57421-9 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 245 10 - TITLE STATEMENT | |
| Title | Proceedings of ELM-2016 |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2018. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XIII, 285 p. 143 illus., 126 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Proceedings in Adaptation, Learning and Optimization, |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This book contains some selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December 13-15, 2016. This conference will provide a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. Extreme Learning Machines (ELM) aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism. ELM represents a suite of (machine or possibly biological) learning techniques in which hidden neurons need not be tuned. ELM learning theories show that very effective learning algorithms can be derived based on randomly generated hidden neurons (with almost any nonlinear piecewise activation functions), independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that “random hidden neurons” capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. ELM offers significant advantages over conventional neural network learning algorithms such as fast learning speed, ease of implementation, and minimal need for human intervention. ELM also shows potential as a viable alternative technique for large‐scale computing and artificial intelligence. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM. . |
| 700 1# - AUTHOR 2 | |
| Author 2 | Cao, Jiuwen. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Cambria, Erik. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Lendasse, Amaury. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Miche, Yoan. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Vong, Chi Man. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-319-57421-9 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2018. |
| 336 ## - | |
| -- | text |
| -- | txt |
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| 337 ## - | |
| -- | computer |
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| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2363-6092 ; |
| 912 ## - | |
| -- | ZDB-2-ENG |
| 912 ## - | |
| -- | ZDB-2-SXE |
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