Abstract
Sensitivity refers to how a neural network output is influenced by its input and/or weight perturbations. Sensitivity analysis dates back to the 1960s, when Widrow investigated the probability of misclassification caused by weight perturbations, which are caused by machine imprecision and noisy input (Widrow and Hoff, 1960). In network hardware realization, such perturbations must be analyzed prior to its design, since they significantly affect network training and generalization. The initial idea of sensitivity analysis has been extended to the optimization of neural networks, such as through sample reduction, feature selection, and critical vector learning.
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© 2009 Springer-Verlag Berlin Heidelberg
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Yeung, D.S., Cloete, I., Shi, D., Ng, W.W. (2009). Principles of Sensitivity Analysis. In: Sensitivity Analysis for Neural Networks. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02532-7_2
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DOI: https://doi.org/10.1007/978-3-642-02532-7_2
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02531-0
Online ISBN: 978-3-642-02532-7
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