Skip to main content
SpringerLink
Log in

Open Issues in Pattern Recognition

  • Conference paper
Computer Recognition Systems

Part of the book series: Advances in Soft Computing ((AINSC,volume 30))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arkedev AG and Braverman EM (1966) Computers and Pattern Recognition. Thompson. Washington, DC.

    Google Scholar 

  2. Bishop CM (1995), Neural Networks for Pattern Recognition. Clarendon Press.

    Google Scholar 

  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. Duda RO, Hart PE and Stork DG (2001). Pattern Classification 2nd. edition, John Wiley & Sons.

    Google Scholar 

  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.

    Article  MATH  Google Scholar 

  6. Duin RPW (2002), The Combining Classifier: To Train Or Not To Train? ICPR2002 II:765–770.

    Google Scholar 

  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. Duin RPW, Pekalska E, and Tax DMJ (2004) The characterization of classification problems by classifier disagreements. Proc. ICPR II:140–143.

    Google Scholar 

  9. Edelman S (1999), Representation and Recognition in Vision, MIT Press.

    Google Scholar 

  10. Fred A, and Jain AK (2002), Data clustering using evidence accumulation, ICPR2002. Quebec City, Canada. 276–280.

    Google Scholar 

  11. Fung, GM and Mangasarian OL (2004), A Feature Selection Newton Method for Support Vector Machine Classification. Computational Optimization and Aplications 28:185–202.

    Article  MATH  MathSciNet  Google Scholar 

  12. Goldfarb L, (1990), On the foundations of intelligent processes — I. An evolving model for pattern recognition. Pattern Recognition. 23(6)595–616.

    Article  MathSciNet  Google Scholar 

  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. 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. 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. 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. Ho TK, and Basu M (2002) Complexity measures of supervised classification problems. IEEE T-PAMI 24:289–300.

    Google Scholar 

  18. Jain AK, Duin RPW and Mao J (2000) Statistical Pattern Recognition: A Review. IEEE T-PAMI 22:4–37.

    Google Scholar 

  19. Kittler J, Hatef M, Duin RPW, and Matas J (1998) On Combining Classifiers, IEEE T-PAMI 20:226–239.

    Google Scholar 

  20. Kuncheva LI (2004) Combining Pattern Classifiers: Methods and Algorithms. Wiley. New York.

    MATH  Google Scholar 

  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. 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. Pekalska E, Duin RPW, and PaclÍk P (2004) Prototype Selection for Dissimilarity-based Classifiers. Pattern Recognition. accepted.

    Google Scholar 

  24. Pekalska E (2005) Dissimilarity representations in pattern recognition. Concepts, theory and applications. PhD thesis. Delft University of Technology.

    Google Scholar 

  25. Ripley BD (1996) Pattern Recognition and Neural Networks. Cambridge University Press. Cambridge.

    MATH  Google Scholar 

  26. Shawe-Taylor J and Cristianini N (2004) Kernel Methods for Pattern Analysis. Cambridge University Press.

    Google Scholar 

  27. Tax DMJ (2001) One-class classification. PhD thesis. Delft Univ. of Technology.

    Google Scholar 

  28. Tax DMJ, and Duin RPW (2004) Support vector data description. Machine Learning 54(1):45–56.

    Article  MATH  Google Scholar 

  29. Webb A (2002) Statistical pattern recognition. Wiley. New York.

    MATH  Google Scholar 

  30. Wolpert DH (1995) The Mathematics of Generalization. Addison-Wesley.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ICT group, Faculty of Electr. Eng., Mathematics and Computer Science, Delft University of Technology, The Netherlands

    Robert P. W. Duin & Elżbieta Pekalska

  2. School of Computer Science, University of Manchester, UK

    Elżbieta Pekalska

Authors
  1. Robert P. W. Duin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elżbieta Pekalska
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Electronics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Marek Kurzyński , Edward Puchała , Michał Woźniak  & Andrzej żołnierek , ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Duin, R.P.W., Pekalska, E. (2005). Open Issues in Pattern Recognition. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_3

Download citation

  • DOI: https://doi.org/10.1007/3-540-32390-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25054-8

  • Online ISBN: 978-3-540-32390-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation