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| LEADER |
01927naaaa2200373uu 4500 |
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23293 |
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20200318 |
| 020 |
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|a 978-3-662-58485-9
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| 024 |
7 |
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|a 10.1007/978-3-662-58485-9
|c doi
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|h English
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|a dc
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| 100 |
1 |
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|a Beyerer, Jürgen
|e edt
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| 856 |
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|z Get fulltext
|u http://library.oapen.org/handle/20.500.12657/23293
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1 |
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|a Kühnert, Christian
|e edt
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|a Niggemann, Oliver
|e edt
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1 |
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|a Beyerer, Jürgen
|e oth
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| 700 |
1 |
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|a Kühnert, Christian
|e oth
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| 700 |
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|a Niggemann, Oliver
|e oth
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| 245 |
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|a Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2018
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| 260 |
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|a Berlin, Heidelberg
|b Springer Nature
|c 2019
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| 300 |
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|a 1 electronic resource (136 p.)
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| 506 |
0 |
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|a Open Access
|2 star
|f Unrestricted online access
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|a This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
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|a Creative Commons
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| 546 |
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|a English
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| 650 |
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|a Communications engineering / telecommunications
|2 bicssc
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| 650 |
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|a Data mining
|2 bicssc
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|a Computer networking & communications
|2 bicssc
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| 650 |
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|a Artificial intelligence
|2 bicssc
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|a Engineering
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| 653 |
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|a Computational intelligence
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|a Computer organization
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|a Electrical engineering
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|a Data mining
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