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...

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Bibliographic Details
Main Author: Hurtado, Jorge E.
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Berlin ; New York : Springer, [2004]
Series:Lecture notes in applied and computational mechanics ; volume 17.
Subjects:
Online Access:Connect to the full text of this electronic book

MARC

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245 1 0 |a Structural reliability :  |b statistical learning perspectives /  |c Jorge E. Hurtado. 
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490 1 |a Lecture notes in applied and computational mechanics ;  |v volume 17 
504 |a Includes bibliographical references (pages 241-250). 
505 0 |a A Discussion on Structural Reliabilty Methods -- Fundamental Concepts of Statistical Learning -- Dimension Reduction and Data Compression -- Classification Methods I - Neural Networks -- Classification Methods II - Support Vector Machines -- Regression Methods -- Classification Approaches to Reliability Indexation. 
520 |a 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. 
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