Construction of nonlinear discrimination function based on the MDL criterion

Manabu Sato; Mineichi Kudo; Jun Toyama; Masaru Shimbo

Kybernetika (1998)

  • Volume: 34, Issue: 4, page [467]-472
  • ISSN: 0023-5954

Abstract

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Although a nonlinear discrimination function may be superior to linear or quadratic classifiers, it is difficult to construct such a function. In this paper, we propose a method to construct a nonlinear discrimination function using Legendre polynomials. The selection of an optimal set of Legendre polynomials is determined by the MDL (Minimum Description Length) criterion. Results using many real data show the effectiveness of this method.

How to cite

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Sato, Manabu, et al. "Construction of nonlinear discrimination function based on the MDL criterion." Kybernetika 34.4 (1998): [467]-472. <http://eudml.org/doc/33379>.

@article{Sato1998,
abstract = {Although a nonlinear discrimination function may be superior to linear or quadratic classifiers, it is difficult to construct such a function. In this paper, we propose a method to construct a nonlinear discrimination function using Legendre polynomials. The selection of an optimal set of Legendre polynomials is determined by the MDL (Minimum Description Length) criterion. Results using many real data show the effectiveness of this method.},
author = {Sato, Manabu, Kudo, Mineichi, Toyama, Jun, Shimbo, Masaru},
journal = {Kybernetika},
keywords = {nonlinear discrimination function; Legendre polynomials; nonlinear discrimination function; Legendre polynomials},
language = {eng},
number = {4},
pages = {[467]-472},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Construction of nonlinear discrimination function based on the MDL criterion},
url = {http://eudml.org/doc/33379},
volume = {34},
year = {1998},
}

TY - JOUR
AU - Sato, Manabu
AU - Kudo, Mineichi
AU - Toyama, Jun
AU - Shimbo, Masaru
TI - Construction of nonlinear discrimination function based on the MDL criterion
JO - Kybernetika
PY - 1998
PB - Institute of Information Theory and Automation AS CR
VL - 34
IS - 4
SP - [467]
EP - 472
AB - Although a nonlinear discrimination function may be superior to linear or quadratic classifiers, it is difficult to construct such a function. In this paper, we propose a method to construct a nonlinear discrimination function using Legendre polynomials. The selection of an optimal set of Legendre polynomials is determined by the MDL (Minimum Description Length) criterion. Results using many real data show the effectiveness of this method.
LA - eng
KW - nonlinear discrimination function; Legendre polynomials; nonlinear discrimination function; Legendre polynomials
UR - http://eudml.org/doc/33379
ER -

References

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  1. Murphy P. M., Aha D. W., UCI Repository of Machine Learning Databases [Machine–Readable Data Repository], University of California, Department of Information and Computer Science, Irvine 1991 
  2. Rissanen J., 10.1214/aos/1176346150, Ann. Statist. 11 (1983), 416–431 (1983) Zbl0513.62005MR0696056DOI10.1214/aos/1176346150
  3. Stone M., Cross–Validatory choice and assessment of statistical predictions, J. Roy. Statist. Soc. 36 (1974), 111–147 (1974) Zbl0308.62063MR0356377

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