A Simple Implementation of the Stochastic Discrimination for Pattern Recognition

  • Dechang Chen
  • Xiuzhen Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)

Abstract

The method of stochastic discrimination (SD) introduced by Kleinberg ([6,7]) is a new method in pattern recognition. It works by producing weak classifiers and then combining them via the Central Limit Theorem to form a strong classifier. SD is overtraining-resistant, has a high convergence rate, and can work quite well in practice. However, some strict assumptions involved in SD and the difficulties in understanding SD have limited its practical use. In this paper, we present a simple algorithm of SD for two-class pattern recognition. We illustrate the algorithm by applications in classifying the feature vectors from some real and simulated data sets. The experimental results show that SD is fast, effective, and applicable.

Keywords

Feature Vector Test Error Rectangular Region High Convergence Rate Handwritten Digit Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Berlind, R.: An Alternative Method of Stochastic Discrimination with Applications to Pattern Recognition. Doctoral Dissertation, Dept. of Mathematics, State University of New York at Buffalo (1994)Google Scholar
  2. 2.
    Chen, D., Huang, P., Cheng, X.: A Concrete Statistical Realization of Kleinberg’s Stochastic Discrimination for Pattern Recognition, Part I. Two-Class Classification. Submitted for PublicationGoogle Scholar
  3. 3.
    Ho, T. K.: Random Decision Forests. In: Kavanaugh, M., Storms, P. (eds.): Proceedings of the Third International Conference on Document Analysis and Recognition. IEEE Computer Society Press, New York (1995) 278–282Google Scholar
  4. 4.
    Ho, T. K.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (1998) 832–844CrossRefGoogle Scholar
  5. 5.
    Johnson, R. A., Wichern, D. W.: Applied Multivariate Statistical Analysis. 4th edn. Prentice Hall (1998)Google Scholar
  6. 6.
    Kleinberg, E. M.: Stochastic Discrimination. Annals of Mathematics and Artificial Intelligence 1 (1990) 207–239Google Scholar
  7. 7.
    Kleinberg, E.M.: An Overtraining-Resistant Stochastic Modeling Method for Pattern Recognition. Annals of Statistics 24 (1996) 2319–2349MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Kleinberg, E. M., Ho, T. K.: Building Projectable Classifiers of Arbitrary Complexity. In: Kavanaugh, M. E., Werner, B. (eds.): Proceedings of the 13th International Conference on Pattern Recognition. IEEE Computer Society Press, New York (1996) 880–885Google Scholar
  9. 9.
    Ripley, B. D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Dechang Chen
    • 1
  • Xiuzhen Cheng
    • 2
  1. 1.University of Wisconsin - Green BayGreen BayUSA
  2. 2.University of MinnesotaMinneapolisUSA

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