Open Issues in Pattern Recognition

  • Robert P. W. Duin
  • Elżbieta Pekalska
Part of the Advances in Soft Computing book series (AINSC, volume 30)


The area of pattern recognition has developed itself into a mature engineering eld with many practical applications. This increased applicability, together with the development of sensors and computer resources, leads to new research areas and raises new questions. In this paper, old and new open issues are discussed that have to be faced in advancing real world applications. Some may only be overcome by brute force procedures, while others may be solved or circumvented either by novel and better procedures, or by a better understanding of their causes. Here, we will try to identify a number of open issues and define them as well as possible.


Pattern Recognition Recognition System Open Issue Classification Procedure Generalization Procedure 
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.


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  1. 1.
    Arkedev AG and Braverman EM (1966) Computers and Pattern Recognition. Thompson. Washington, DC.Google Scholar
  2. 2.
    Bishop CM (1995), Neural Networks for Pattern Recognition. Clarendon Press.Google Scholar
  3. 3.
    de Diego IM, Moguerza JM, and Muñoz A (2004) Combining Kernel Information for Support Vector Classification. Multiple Classifier Systems. LNCS:3077. Springer-Verlag. 102–111.Google Scholar
  4. 4.
    Duda RO, Hart PE and Stork DG (2001). Pattern Classification 2nd. edition, John Wiley & Sons.Google Scholar
  5. 5.
    Duin RPW, Roli F, and De Ridder D (2002). A note on core research issues for statistical pattern recognition, Pattern Recognition Letters, 23:493–499.zbMATHCrossRefGoogle Scholar
  6. 6.
    Duin RPW (2002), The Combining Classifier: To Train Or Not To Train? ICPR2002 II:765–770.Google Scholar
  7. 7.
    Duin RPW, Pekalska E, PaclÍk P, and Tax DMJ (2004). The dissimilarity representation, a basis for domain based pattern recognition?, In: Pattern representation and the future of pattern recognition, Workshop ICPR2004. 43–56.Google Scholar
  8. 8.
    Duin RPW, Pekalska E, and Tax DMJ (2004) The characterization of classification problems by classifier disagreements. Proc. ICPR II:140–143.Google Scholar
  9. 9.
    Edelman S (1999), Representation and Recognition in Vision, MIT Press.Google Scholar
  10. 10.
    Fred A, and Jain AK (2002), Data clustering using evidence accumulation, ICPR2002. Quebec City, Canada. 276–280.Google Scholar
  11. 11.
    Fung, GM and Mangasarian OL (2004), A Feature Selection Newton Method for Support Vector Machine Classification. Computational Optimization and Aplications 28:185–202.zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Goldfarb L, (1990), On the foundations of intelligent processes — I. An evolving model for pattern recognition. Pattern Recognition. 23(6)595–616.CrossRefMathSciNetGoogle Scholar
  13. 13.
    Goldfarb L, and Hook J (1998), Why classical models for pattern recognition are not pattern recognition models. In: International Conference on Advances in Pattern Recognition. Springer. 405–414.Google Scholar
  14. 14.
    Goldfarb L, Gay D, Golubitsky O, and Korkin D (2004), What is a structural representation? 2nd version. Faculty of Computer Science, UNB. Technical Report TR04-165.Google Scholar
  15. 15.
    Graepel T, Herbrich R, Schölkopf B, Smola A, Bartlett P, Müller KR, Obermayer K and Williamson R (1999) Classification on Proximity Data with LP-Machines. ICANN 1991, 304–309.Google Scholar
  16. 16.
    Van der Heijden F, Duin RPW, de Ridder D and Tax DMJ (2004) Classification, Parameter Estimation and State Estimation. An Engineering Approach using Matlab. John Wiley & Sons Ltd.Google Scholar
  17. 17.
    Ho TK, and Basu M (2002) Complexity measures of supervised classification problems. IEEE T-PAMI 24:289–300.Google Scholar
  18. 18.
    Jain AK, Duin RPW and Mao J (2000) Statistical Pattern Recognition: A Review. IEEE T-PAMI 22:4–37.Google Scholar
  19. 19.
    Kittler J, Hatef M, Duin RPW, and Matas J (1998) On Combining Classifiers, IEEE T-PAMI 20:226–239.Google Scholar
  20. 20.
    Kuncheva LI (2004) Combining Pattern Classifiers: Methods and Algorithms. Wiley. New York.zbMATHGoogle Scholar
  21. 21.
    Pekalska E, Skurichina M, and Duin RPW (2004) Combining Dissimilarity Representations in One-class Classifier Problems. Multiple Classifier Systems. LNCS:3077. Springer-Verlag. 122–133.Google Scholar
  22. 22.
    Pekalska E, Duin RPW, Gunter S, and Bunke H (2004) On not making dissimilarities Euclidean. In: Structural, Syntactic, and Statistical Pattern Recognition. LNCS:3138. Springer Verlag, Berlin. 1145–1154.Google Scholar
  23. 23.
    Pekalska E, Duin RPW, and PaclÍk P (2004) Prototype Selection for Dissimilarity-based Classifiers. Pattern Recognition. accepted.Google Scholar
  24. 24.
    Pekalska E (2005) Dissimilarity representations in pattern recognition. Concepts, theory and applications. PhD thesis. Delft University of Technology.Google Scholar
  25. 25.
    Ripley BD (1996) Pattern Recognition and Neural Networks. Cambridge University Press. Cambridge.zbMATHGoogle Scholar
  26. 26.
    Shawe-Taylor J and Cristianini N (2004) Kernel Methods for Pattern Analysis. Cambridge University Press.Google Scholar
  27. 27.
    Tax DMJ (2001) One-class classification. PhD thesis. Delft Univ. of Technology.Google Scholar
  28. 28.
    Tax DMJ, and Duin RPW (2004) Support vector data description. Machine Learning 54(1):45–56.zbMATHCrossRefGoogle Scholar
  29. 29.
    Webb A (2002) Statistical pattern recognition. Wiley. New York.zbMATHGoogle Scholar
  30. 30.
    Wolpert DH (1995) The Mathematics of Generalization. Addison-Wesley.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Robert P. W. Duin
    • 1
  • Elżbieta Pekalska
    • 1
    • 2
  1. 1.ICT group, Faculty of Electr. Eng., Mathematics and Computer ScienceDelft University of TechnologyThe Netherlands
  2. 2.School of Computer ScienceUniversity of ManchesterUK

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