Efficient model learning for Gaussian processes with transformers, kernel-kernels and active learning:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin
Technische Universität Berlin
2024
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| Schlagworte: | |
| Online-Zugang: | kostenfrei kostenfrei kostenfrei https://d-nb.info/1352075458/34 |
| Beschreibung: | 1 Online-Ressource (xviii, 134 Seiten) Illustrationen, Diagramme |
| DOI: | 10.14279/depositonce-22345 |
Internformat
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Datensatz im Suchindex
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| spelling | Bitzer, Matthias Verfasser aut Efficient model learning for Gaussian processes with transformers, kernel-kernels and active learning vorgelegt von M. Sc. Matthias Bitzer Effizientes Modell-Lernen für Gauß-Prozesse mit Transformern, Kernel-Kernel und aktivem Lernen Berlin Technische Universität Berlin 2024 1 Online-Ressource (xviii, 134 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Dissertation Technische Universität Berlin 2024 Archivierung/Langzeitarchivierung gewährleistet DE-101 pdager 2\p Gauß-Prozess (DE-588)4156111-9 gnd 3\p Maschinelles Lernen (DE-588)4193754-5 gnd 4\p Kernfunktion (DE-588)4163607-7 gnd 5\p Support-Vektor-Maschine (DE-588)4505517-8 gnd 6\p Neuronales Netz (DE-588)4226127-2 gnd (DE-588)4113937-9 Hochschulschrift gnd-content Erscheint auch als Druck-Ausgabe (DE-604)BV050112873 https://doi.org/10.14279/depositonce-22345 Resolving-System kostenfrei Volltext https://hdl.handle.net/11303/23531 Resolving-System kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:101:1-2412250104483.534096522595 Resolving-System kostenfrei Volltext https://d-nb.info/1352075458/34 Langzeitarchivierung Nationalbibliothek 1\p emasg 0,49368 20241225 DE-101 https://d-nb.info/provenance/plan#emasg 2\p emagnd 0,28070 20241225 DE-101 https://d-nb.info/provenance/plan#emagnd 3\p emagnd 0,12521 20241225 DE-101 https://d-nb.info/provenance/plan#emagnd 4\p emagnd 0,10174 20241225 DE-101 https://d-nb.info/provenance/plan#emagnd 5\p emagnd 0,06254 20241225 DE-101 https://d-nb.info/provenance/plan#emagnd 6\p emagnd 0,04663 20241225 DE-101 https://d-nb.info/provenance/plan#emagnd |
| spellingShingle | Bitzer, Matthias Efficient model learning for Gaussian processes with transformers, kernel-kernels and active learning 2\p Gauß-Prozess (DE-588)4156111-9 gnd 3\p Maschinelles Lernen (DE-588)4193754-5 gnd 4\p Kernfunktion (DE-588)4163607-7 gnd 5\p Support-Vektor-Maschine (DE-588)4505517-8 gnd 6\p Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4156111-9 (DE-588)4193754-5 (DE-588)4163607-7 (DE-588)4505517-8 (DE-588)4226127-2 (DE-588)4113937-9 |
| title | Efficient model learning for Gaussian processes with transformers, kernel-kernels and active learning |
| title_alt | Effizientes Modell-Lernen für Gauß-Prozesse mit Transformern, Kernel-Kernel und aktivem Lernen |
| title_auth | Efficient model learning for Gaussian processes with transformers, kernel-kernels and active learning |
| title_exact_search | Efficient model learning for Gaussian processes with transformers, kernel-kernels and active learning |
| title_full | Efficient model learning for Gaussian processes with transformers, kernel-kernels and active learning vorgelegt von M. Sc. Matthias Bitzer |
| title_fullStr | Efficient model learning for Gaussian processes with transformers, kernel-kernels and active learning vorgelegt von M. Sc. Matthias Bitzer |
| title_full_unstemmed | Efficient model learning for Gaussian processes with transformers, kernel-kernels and active learning vorgelegt von M. Sc. Matthias Bitzer |
| title_short | Efficient model learning for Gaussian processes with transformers, kernel-kernels and active learning |
| title_sort | efficient model learning for gaussian processes with transformers kernel kernels and active learning |
| topic | 2\p Gauß-Prozess (DE-588)4156111-9 gnd 3\p Maschinelles Lernen (DE-588)4193754-5 gnd 4\p Kernfunktion (DE-588)4163607-7 gnd 5\p Support-Vektor-Maschine (DE-588)4505517-8 gnd 6\p Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Gauß-Prozess Maschinelles Lernen Kernfunktion Support-Vektor-Maschine Neuronales Netz Hochschulschrift |
| url | https://doi.org/10.14279/depositonce-22345 https://hdl.handle.net/11303/23531 https://nbn-resolving.org/urn:nbn:de:101:1-2412250104483.534096522595 https://d-nb.info/1352075458/34 |
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