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Sensitivity Analysis for Neural Networks
Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing t...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2010.
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| Edición: | 1st ed. 2010. |
| Colección: | Natural Computing Series,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-642-02532-7 | ||
| 003 | DE-He213 | ||
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| 007 | cr nn 008mamaa | ||
| 008 | 100301s2010 gw | s |||| 0|eng d | ||
| 020 | |a 9783642025327 |9 978-3-642-02532-7 | ||
| 024 | 7 | |a 10.1007/978-3-642-02532-7 |2 doi | |
| 050 | 4 | |a Q334-342 | |
| 050 | 4 | |a TA347.A78 | |
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| 100 | 1 | |a Yeung, Daniel S. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Sensitivity Analysis for Neural Networks |h [electronic resource] / |c by Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W. Y. Ng. |
| 250 | |a 1st ed. 2010. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2010. | |
| 300 | |a VIII, 86 p. 24 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Natural Computing Series, |x 2627-6461 | |
| 505 | 0 | |a to Neural Networks -- Principles of Sensitivity Analysis -- Hyper-Rectangle Model -- Sensitivity Analysis with Parameterized Activation Function -- Localized Generalization Error Model -- Critical Vector Learning for RBF Networks -- Sensitivity Analysis of Prior Knowledge1 -- Applications. | |
| 520 | |a Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Control engineering. | |
| 650 | 0 | |a Robotics. | |
| 650 | 0 | |a Automation. | |
| 650 | 0 | |a System theory. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Computer simulation. | |
| 650 | 0 | |a Engineering design. | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Control, Robotics, Automation. |
| 650 | 2 | 4 | |a Complex Systems. |
| 650 | 2 | 4 | |a Automated Pattern Recognition. |
| 650 | 2 | 4 | |a Computer Modelling. |
| 650 | 2 | 4 | |a Engineering Design. |
| 700 | 1 | |a Cloete, Ian. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Shi, Daming. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Ng, Wing W. Y. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642025334 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642025310 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642261398 |
| 830 | 0 | |a Natural Computing Series, |x 2627-6461 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-642-02532-7 |z Texto Completo |
| 912 | |a ZDB-2-SCS | ||
| 912 | |a ZDB-2-SXCS | ||
| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||