Ebook: Structural Reliability: Statistical Learning Perspectives
Author: Dr. Jorge E. Hurtado (auth.)
- Tags: Theoretical and Applied Mechanics, Artificial Intelligence (incl. Robotics), Computational Intelligence, Structural Mechanics
- Series: Lecture Notes in Applied and Computational Mechanics 17
- Year: 2004
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
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.
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.
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.
Content:
Front Matter....Pages I-XIV
A Discussion on Structural Reliability Methods....Pages 1-43
Fundamental Concepts of Statistical Learning....Pages 45-79
Dimension Reduction and Data Compression....Pages 81-105
Classification Methods I — Neural Networks....Pages 107-143
Classification Methods II — Support Vector Machines....Pages 145-190
Regression Methods....Pages 191-218
Classification Approaches to Reliability Indexation....Pages 219-240
Back Matter....Pages 241-257
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.
Content:
Front Matter....Pages I-XIV
A Discussion on Structural Reliability Methods....Pages 1-43
Fundamental Concepts of Statistical Learning....Pages 45-79
Dimension Reduction and Data Compression....Pages 81-105
Classification Methods I — Neural Networks....Pages 107-143
Classification Methods II — Support Vector Machines....Pages 145-190
Regression Methods....Pages 191-218
Classification Approaches to Reliability Indexation....Pages 219-240
Back Matter....Pages 241-257
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