Featureless pattern classification

Robert P. W. Duin; Dick de Ridder; David M. J. Tax

Kybernetika (1998)

  • Volume: 34, Issue: 4, page [399]-404
  • ISSN: 0023-5954

Abstract

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In this paper the possibilities are discussed for training statistical pattern recognizers based on a distance representation of the objects instead of a feature representation. Distances or similarities are used between the unknown objects to be classified with a selected subset of the training objects (the support objects). These distances are combined into linear or nonlinear classifiers. In this approach the feature definition problem is replaced by finding good similarity measures. The proposal corresponds with determining classification functions in Hilbert space using an infinite feature set. It is a direct consequence of Vapnik’s support vector classifier [Vap2].

How to cite

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Duin, Robert P. W., de Ridder, Dick, and Tax, David M. J.. "Featureless pattern classification." Kybernetika 34.4 (1998): [399]-404. <http://eudml.org/doc/33368>.

@article{Duin1998,
abstract = {In this paper the possibilities are discussed for training statistical pattern recognizers based on a distance representation of the objects instead of a feature representation. Distances or similarities are used between the unknown objects to be classified with a selected subset of the training objects (the support objects). These distances are combined into linear or nonlinear classifiers. In this approach the feature definition problem is replaced by finding good similarity measures. The proposal corresponds with determining classification functions in Hilbert space using an infinite feature set. It is a direct consequence of Vapnik’s support vector classifier [Vap2].},
author = {Duin, Robert P. W., de Ridder, Dick, Tax, David M. J.},
journal = {Kybernetika},
keywords = {training statistical pattern; similarity measures; training statistical pattern; similarity measures},
language = {eng},
number = {4},
pages = {[399]-404},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Featureless pattern classification},
url = {http://eudml.org/doc/33368},
volume = {34},
year = {1998},
}

TY - JOUR
AU - Duin, Robert P. W.
AU - de Ridder, Dick
AU - Tax, David M. J.
TI - Featureless pattern classification
JO - Kybernetika
PY - 1998
PB - Institute of Information Theory and Automation AS CR
VL - 34
IS - 4
SP - [399]
EP - 404
AB - In this paper the possibilities are discussed for training statistical pattern recognizers based on a distance representation of the objects instead of a feature representation. Distances or similarities are used between the unknown objects to be classified with a selected subset of the training objects (the support objects). These distances are combined into linear or nonlinear classifiers. In this approach the feature definition problem is replaced by finding good similarity measures. The proposal corresponds with determining classification functions in Hilbert space using an infinite feature set. It is a direct consequence of Vapnik’s support vector classifier [Vap2].
LA - eng
KW - training statistical pattern; similarity measures; training statistical pattern; similarity measures
UR - http://eudml.org/doc/33368
ER -

References

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  1. Aizerman M. A., Braverman E. M., Rozonoer L. I., The probability problem of pattern recognition learning and the method of potential functions, Automat. Remote Control 25 (1964), 1175-1193 (1964) MR0172768
  2. Devijver P. A., Kittler J., Pattern Recognition: A Statistical Approach, Prentice Hall, London 1982 Zbl0542.68071MR0692767
  3. Duin R. P. W., Small sample size generalization, In: SCIA’95, Proc. 9th Scandinavian Conf. on Image Analysis (G. Borgefors, ed.), Volume 2, Uppsala 1995, pp. 957–964 (1995) 
  4. Duin R. P. W., Ridder D. de, Neural network experiences between perceptrons and support vectors, In: Proc. of the 8th British Machine Vision Conference (A. F. Clark, ed.), Volume 2, Colchester 1997, pp. 590–599 (1997) 
  5. Duin R. P. W., Ridder D. de, Tax D. M. J., Experiments with object based discriminant functions; a featureless approach to pattern recognition, In: Pattern Recognition in Practice V, Vlieland, 1997, to be published in Pattern Recognition Letters (1997) 
  6. Duin R. P. W., Ridder D. de, Tax D. M. J., Featureless Classification, In: Proc. 1st International Workshop Statistical Techniques in Pattern Recognition (P. Pudil, J. Novovičová and J. Grim, eds.), Prague 1997, pp. 37–42 (1997) 
  7. Jain A. K., Chandrasekaran B., Dimensionality and sample size considerations in pattern Recognition practice, In: Handbook of Statistics (P. R. Krishnaiah and L. N. Kanal, eds.), Vol. 2, North–Holland, Amsterdam 1987, pp. 835–855 (1987) 
  8. Raudys S., Evolution and generalization of a single neurone, I. Single layer perceptron as seven statistical classifiers. Neural Networks, to be published 
  9. Schölkopf B., Support Vector Learning, Ph.D. Thesis, Techn. Universität Berlin 1997 Zbl0935.68084
  10. Tax D. M. J., Ridder D. de, Duin R. P. W., Support vector classifiers: a first look, In: ASCI’97, Proc. Third Annual Conference of the Advanced School for Computing and Imaging, 1997 
  11. Vapnik V. N., Estimation of Dependences Based on Empirical Data, Springer–Verlag, New York 1982 Zbl1118.62002MR0672244
  12. Vapnik V. N., The Nature of Statistical Learning Theory, Springer–Verlag, Berlin 1995 Zbl0934.62009MR1367965
  13. Wilson C. L., Marris M. D., Handprinted Character Database 2, National Institute of Standards and Technology; Advanced Systems division, 1990 

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