Frontmatter.
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- Automatisierungstechnik, 2019, v. 67, n. 10, p. I, doi. 10.1515/auto-2019-frontmatter10
- Publication type:
- Article
Works matching IS 01782312 AND DT 2019 AND VI 67 AND IP 10
Results: 9
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Fighting the curse of dimensionality with local model networks.
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- Automatisierungstechnik, 2019, v. 67, n. 10, p. 889, doi. 10.1515/auto-2019-0095
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- Publication type:
- Article
3
Ausgewählte Beiträge aus dem GMA-Fachausschuss 5.14 „Computational Intelligence".
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- Automatisierungstechnik, 2019, v. 67, n. 10, p. 817, doi. 10.1515/auto-2019-0101
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- Publication type:
- Article
4
Fuzzy tissue detection for real-time focal control in corneal confocal microscopy.
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- Automatisierungstechnik, 2019, v. 67, n. 10, p. 879, doi. 10.1515/auto-2019-0034
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- Publication type:
- Article
5
Fahrstreifenerkennung mit Deep Learning für automatisierte Fahrfunktionen.
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- Automatisierungstechnik, 2019, v. 67, n. 10, p. 866, doi. 10.1515/auto-2019-0029
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- Publication type:
- Article
6
Zur Homogenisierung von Testsignalen für die nichtlineare Systemidentifikation.
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- Automatisierungstechnik, 2019, v. 67, n. 10, p. 820, doi. 10.1515/auto-2019-0041
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- Publication type:
- Article
7
Fast and simple dataset selection for machine learning.
- Published in:
- Automatisierungstechnik, 2019, v. 67, n. 10, p. 833, doi. 10.1515/auto-2019-0010
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- Publication type:
- Article
8
Automated design process for hybrid regression modeling with a one-class SVM.
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- Automatisierungstechnik, 2019, v. 67, n. 10, p. 843, doi. 10.1515/auto-2019-0013
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- Publication type:
- Article
9
Feature fusion to increase the robustness of machine learners in industrial environments.
- Published in:
- Automatisierungstechnik, 2019, v. 67, n. 10, p. 853, doi. 10.1515/auto-2019-0028
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- Publication type:
- Article