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Nonlinear logistic discrimination via regularized radial basis functions for classifying high-dimensional data

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Author Info
Tomohiro Ando ()
Sadanori Konishi ()
Abstract

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File URL: http://hdl.handle.net/10.1007/s10463-007-0143-3
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Publisher Info
Article provided by Springer in its journal Annals of the Institute of Statistical Mathematics.

Volume (Year): 61 (2009)
Issue (Month): 2 (June)
Pages: 331-353
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Handle: RePEc:spr:aistmt:v:61:y:2009:i:2:p:331-353

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Related research
Keywords: Bayes approach; Information criteria; Maximum penalized likelihood method; Radial basis functions;

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Seiya Imoto & Sadanori Konishi, 2003. "Selection of smoothing parameters inB-spline nonparametric regression models using information criteria," Annals of the Institute of Statistical Mathematics, Springer, vol. 55(4), pages 671-687, December. [Downloadable!] (restricted)
  2. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March. [Downloadable!] (restricted)
  3. Yoshisuke Nonaka & Sadanori Konishi, 2005. "Nonlinear regression modeling using regularized local likelihood method," Annals of the Institute of Statistical Mathematics, Springer, vol. 57(4), pages 617-635, December. [Downloadable!] (restricted)
  4. Sadanori Konishi, 2004. "Bayesian information criteria and smoothing parameter selection in radial basis function networks," Biometrika, Oxford University Press for Biometrika Trust, vol. 91(1), pages 27-43, March.
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This page was last updated on 2009-12-4.

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