Structural Reliability: Statistical Learning Perspectives
This monograph presents an original approach to Structural Reliability from the perspective of Statistical Learning Theory. It proposes new methods for solving the reliability problem utilizing the recent developments in Computational Learning Theory, such as Neural Networks and Support Vector machi...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2004
|
| Series: | Lecture Notes in Applied and Computational Mechanics
17 |
| Subjects: | |
| Online Access: | FHI01 BTU01 Volltext |
| Summary: | This monograph presents an original approach to Structural Reliability from the perspective of Statistical Learning Theory. It proposes new methods for solving the reliability problem utilizing the recent developments in Computational Learning Theory, such as Neural Networks and Support Vector machines. It also demonstrates important issues on the management of samples in Monte Carlo simulation for structural reliability analysis purposes and examines the treatment of the structural reliability problem as a pattern recognition or classification task. This carefully written monograph is aiming at researchers and students in civil and mechanical engineering, especially in reliability engineering, structural analysis, or statistical learning |
| Physical Description: | 1 Online-Ressource (XIV, 257 p) |
| ISBN: | 9783540409878 |
| DOI: | 10.1007/978-3-540-40987-8 |
Staff View
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:03Z |
| institution | BVB |
| isbn | 9783540409878 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030538886 |
| oclc_num | 1184486764 |
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| owner_facet | DE-573 DE-634 |
| physical | 1 Online-Ressource (XIV, 257 p) |
| psigel | ZDB-2-ENG ZDB-2-ENG_2000/2004 ZDB-2-ENG ZDB-2-ENG_2000/2004 ZDB-2-ENG ZDB-2-ENG_Archiv |
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| publisher | Springer Berlin Heidelberg |
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| series2 | Lecture Notes in Applied and Computational Mechanics |
| spelling | Hurtado, Jorge E. Verfasser aut Structural Reliability Statistical Learning Perspectives by Jorge E. Hurtado Berlin, Heidelberg Springer Berlin Heidelberg 2004 1 Online-Ressource (XIV, 257 p) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Applied and Computational Mechanics 17 This monograph presents an original approach to Structural Reliability from the perspective of Statistical Learning Theory. It proposes new methods for solving the reliability problem utilizing the recent developments in Computational Learning Theory, such as Neural Networks and Support Vector machines. It also demonstrates important issues on the management of samples in Monte Carlo simulation for structural reliability analysis purposes and examines the treatment of the structural reliability problem as a pattern recognition or classification task. This carefully written monograph is aiming at researchers and students in civil and mechanical engineering, especially in reliability engineering, structural analysis, or statistical learning Engineering Building Construction Theoretical and Applied Mechanics Artificial Intelligence (incl. Robotics) Computational Intelligence Structural Mechanics Artificial intelligence Computational intelligence Mechanics Mechanics, Applied Structural mechanics Buildings / Design and construction Building Construction Engineering, Architectural Strukturmechanik (DE-588)4126904-4 gnd rswk-swf Zuverlässigkeit (DE-588)4059245-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Strukturmechanik (DE-588)4126904-4 s Zuverlässigkeit (DE-588)4059245-5 s Maschinelles Lernen (DE-588)4193754-5 s 1\p DE-604 Erscheint auch als Druck-Ausgabe 9783642535765 https://doi.org/10.1007/978-3-540-40987-8 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hurtado, Jorge E. Structural Reliability Statistical Learning Perspectives Engineering Building Construction Theoretical and Applied Mechanics Artificial Intelligence (incl. Robotics) Computational Intelligence Structural Mechanics Artificial intelligence Computational intelligence Mechanics Mechanics, Applied Structural mechanics Buildings / Design and construction Building Construction Engineering, Architectural Strukturmechanik (DE-588)4126904-4 gnd Zuverlässigkeit (DE-588)4059245-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4126904-4 (DE-588)4059245-5 (DE-588)4193754-5 |
| title | Structural Reliability Statistical Learning Perspectives |
| title_auth | Structural Reliability Statistical Learning Perspectives |
| title_exact_search | Structural Reliability Statistical Learning Perspectives |
| title_full | Structural Reliability Statistical Learning Perspectives by Jorge E. Hurtado |
| title_fullStr | Structural Reliability Statistical Learning Perspectives by Jorge E. Hurtado |
| title_full_unstemmed | Structural Reliability Statistical Learning Perspectives by Jorge E. Hurtado |
| title_short | Structural Reliability |
| title_sort | structural reliability statistical learning perspectives |
| title_sub | Statistical Learning Perspectives |
| topic | Engineering Building Construction Theoretical and Applied Mechanics Artificial Intelligence (incl. Robotics) Computational Intelligence Structural Mechanics Artificial intelligence Computational intelligence Mechanics Mechanics, Applied Structural mechanics Buildings / Design and construction Building Construction Engineering, Architectural Strukturmechanik (DE-588)4126904-4 gnd Zuverlässigkeit (DE-588)4059245-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Engineering Building Construction Theoretical and Applied Mechanics Artificial Intelligence (incl. Robotics) Computational Intelligence Structural Mechanics Artificial intelligence Computational intelligence Mechanics Mechanics, Applied Structural mechanics Buildings / Design and construction Building Construction Engineering, Architectural Strukturmechanik Zuverlässigkeit Maschinelles Lernen |
| url | https://doi.org/10.1007/978-3-540-40987-8 |
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