Sensitivity Analysis for Neural Networks: Natural Computing Series
Autor Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W. Y. Ngen Limba Engleză Hardback – 18 noi 2009
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.
Toate formatele și edițiile | Preț | Express |
---|---|---|
Paperback (1) | 622.71 lei 6-8 săpt. | |
Springer Berlin, Heidelberg – 14 mar 2012 | 622.71 lei 6-8 săpt. | |
Hardback (1) | 627.24 lei 6-8 săpt. | |
Springer Berlin, Heidelberg – 18 noi 2009 | 627.24 lei 6-8 săpt. |
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Specificații
ISBN-13: 9783642025310
ISBN-10: 3642025315
Pagini: 96
Ilustrații: VIII, 86 p. 24 illus.
Dimensiuni: 155 x 235 x 10 mm
Greutate: 0.27 kg
Ediția:2010
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Natural Computing Series
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3642025315
Pagini: 96
Ilustrații: VIII, 86 p. 24 illus.
Dimensiuni: 155 x 235 x 10 mm
Greutate: 0.27 kg
Ediția:2010
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Natural Computing Series
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
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.
Recenzii
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)
“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)
Caracteristici
This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. Includes supplementary material: sn.pub/extras