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Machine Learning for Cyber Physical Systems
Machine Learning for Cyber Physical Systems
Knygos.lt klubas Knygos.lt nariams
75,94 €
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This open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. 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 a…
  • Leidėjas:
  • ISBN-10: 3662627450
  • ISBN-13: 9783662627457
  • Formatas: 19.3 x 23.9 x 0.8 cm, minkšti viršeliai
  • Kalba: Anglų

Machine Learning for Cyber Physical Systems (el. knyga) (skaityta knyga) | knygos.lt

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This open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020.

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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  • Leidėjas:
  • ISBN-10: 3662627450
  • ISBN-13: 9783662627457
  • Formatas: 19.3 x 23.9 x 0.8 cm, minkšti viršeliai
  • Kalba: Anglų

This open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020.

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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