Skip to main content
SpringerLink
Log in

A Bayes Algorithm for the Multitask Pattern Recognition Problem – Direct and Decomposed Independent Approaches

  • Conference paper
Computational Science and Its Applications – ICCSA 2004 (ICCSA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3046))

Included in the following conference series:

Abstract

The paper presents algorithms of the multitask recognition for the direct approach and for the decomposed independent approach. Both algorithms are presented in the even of full probabilistic information. Algorithms with full probabilistic information were working on basis of Bayes decision theory. Full probabilistic information in a pattern recognition task, denotes the knowledge of the classes probabilities and the class-conditional probability density functions. Optimal algorithms for the selected loss function will be presented.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Gola, M., Kasprzak, A.: The Two-Criteria Topological Design Problem in WAN with Delay Constraint: An Algorithm and Computational Results. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds.) ICCSA 2003. LNCS, vol. 2667, pp. 180–189. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  2. Wozniak, M.: Proposition of the quality measure for the probabilistic decision support system. In: Chung, P.W.H., Hinde, C.J., Ali, M. (eds.) IEA/AIE 2003. LNCS (LNAI), vol. 2718, pp. 686–691. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  3. Puchala, E., Kurzynski, M.: A branch-and-bound algorithm for optimization of multiperspective classifier. In: Proceedings of the 12th IAPR, Jerusalem, Israel, pp. 235–239 (1994)

    Google Scholar 

  4. Kurzynski, M., Puchala, E.: Algorithms of the multiperspective recognition. In: Proc. of the 11th Int. Conf. on Pattern Recognition, Hague (1992)

    Google Scholar 

  5. Duda, R., Hart, P.: Pattern classification and scene analysis. John Wiley & Sons, New York (1973)

    MATH  Google Scholar 

  6. Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, New York (1972)

    Google Scholar 

  7. Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Statist. 33, 1065–1076 (1962)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chair of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Edward Puchala

Authors
  1. Edward Puchala
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  2. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  5. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  6. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Puchala, E. (2004). A Bayes Algorithm for the Multitask Pattern Recognition Problem – Direct and Decomposed Independent Approaches. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24768-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-24768-5_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22060-2

  • Online ISBN: 978-3-540-24768-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation