Abstract
The curse of dimensionality is always problematic in pattern classification problems. In this chapter, we provide a brief comparison of the major methodologies for reducing input dimensionality and summarize them in three categories: correlation among features, transformation and neural network sensitivity analysis. Furthermore, we propose a novel method for reducing input dimensionality that uses a stochastic RBFNN sensitivity measure. The experimental results are promising for our method of reducing input dimensionality.
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© 2009 Springer-Verlag Berlin Heidelberg
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Yeung, D.S., Cloete, I., Shi, D., Ng, W.W. (2009). Applications. In: Sensitivity Analysis for Neural Networks. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02532-7_8
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DOI: https://doi.org/10.1007/978-3-642-02532-7_8
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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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