Sensitivity Analysis for Neural Networks
Language: English
Published by Springer, Springer Nov 2009, 2009
Series: Book 8 of 32 - Natural Computing
- Hardcover
- New
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Seller Inventory # 9783642025310
- Title
- Sensitivity Analysis for Neural Networks
- Author
- Daniel S. Yeung
- Publisher
- Springer, Springer Nov 2009
- Publication year
- 2009
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3642025315
- ISBN 13
- 9783642025310
- Item weight
- 319 grams
- Dimensions
- 241x160x9 mm
- Series
- Book 8 of 32: Natural Computing
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.
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Review
From the reviews:
“Neural Networks are seen as an information paradigm inspired by the way the human brain processes information. ... The book may be used by researchers in diverse domains, such as neural networks, machine learning, computer engineering, etc., facing problems connected to sensitivity analysis of neural networks.” (Florin Gorunescu, Zentralblatt MATH, Vol. 1189, 2010)"About the title" may belong to another edition of this title.
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