Skip to main content
SpringerLink
Log in
Book cover

Mexican International Conference on Artificial Intelligence

MICAI 2002: MICAI 2002: Advances in Artificial Intelligence pp 282–291Cite as

Improving Pattern Recognition Using Several Feature Vectors

  • Conference paper
  • First Online:

  • 650 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2313))

Abstract

Most pattern recognition systems use only one feature vector to describe the objects to be recognized. In this paper we suggest to use more than one feature vector to improve the classification results. The use of several feature vectors require a special neural network, a supervised ART2 NN is used [1]. The performance of a supervised or unsupervised ART2 NN depends on the appropriate selection of the vigilance threshold. If the value is near to zero, a lot of clusters will be generated, but if it is greater, then must clusters will be generated. A methodology to select this threshold was first proposed in [2]. The advantages to use several feature vectors instead of only one are shown on this work. We show some results in the case of character recognition using one and two feature vectors. We also compare the performance of our proposal with the multilayer perceptron.

This is a preview of subscription content, 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. H. Sossa, P. Rayón y J. Figueroa. Arquitectura basada en redes neuronales para el reconocimiento de rostros. Soluciones Avanzadas. Año 7, No. 63. Noviembre 1998.

    Google Scholar 

  2. P. Rayón and H. Sossa. A procedure to select the vigilance threshold for the ART2 for supervised and unsupervised training. LNAI 1793, pp. 389–400, Springer Verlag, 2000.

    Google Scholar 

  3. G.A. Carpenter and S. Grossberg, ART2: self-organizing of stable category recognition codes for analog input patterns. Applied Optics, 26, pp. 4919–4930, 1987.

    Article  Google Scholar 

  4. R. Schalkoff, Pattern Recognition: statistical, structural and neural approaches. John Wiley & Sons, Inc., 1992.

    Google Scholar 

  5. J.A. Freeman and D. M. Skapura, Neural Networks: algorithms, applications and programming techniques. Addison Wesley, 1992.

    Google Scholar 

  6. A.S. Pandya and R. B. Macy, Pattern Recognition with Neural Networks in C++. IEEE Press, 1995.

    Google Scholar 

  7. G. Francis, The stability-plasticity dilemma in competitive learning. Department of Psychology Sciences. Purdue University. Technical Report No. 96–1, 1996.

    Google Scholar 

  8. C.G. Looney, Pattern Recognition using Neural Networks. Oxford University Press, 1999.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Insituto Tecnológico y de Estudios Superiores de Monterrey, Campus Ciudad de México, Calle del Puente 222, Ejidos de Huipulco, México, 14380 D.F., México

    Patricia Rayón Villela

  2. Centro de Investigación en Computación-IPN, Av. Juan de Dios Batíz, esquina con Miguel Otón de Mendizábal, México, D.F. 07738, México

    Juan Humberto Sossa Azuela

Authors
  1. Patricia Rayón Villela
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan Humberto Sossa Azuela
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computer Science Section, Electrical Engineering Department, CINVESTAV-IPN, Av. IPN 2508, Col. San Pedro Zacatenco, D.F. 07300, Mexico, Mexico

    Carlos A. Coello Coello

  2. Computer Science Department, ITESM-Mexico City, Calle del Puente 222, Tlalpan, D.F. 14380, Mexico, Mexico

    Alvaro de Albornoz

  3. Computer Science Department, ITESM-Cuernavaca, Reforma 182-A, Lomas de Cuernavaca, Temixco, 62589, Morelos, Mexico

    Luis Enrique Sucar

  4. Department of Computer Science, ITAM, Rio Hondo 1, Progreso Tizapan, D.F. 01000, Mexico, Mexico

    Osvaldo Cairó Battistutti

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rayón Villela, P., Sossa Azuela, J.H. (2002). Improving Pattern Recognition Using Several Feature Vectors. In: Coello Coello, C.A., de Albornoz, A., Sucar, L.E., Battistutti, O.C. (eds) MICAI 2002: Advances in Artificial Intelligence. MICAI 2002. Lecture Notes in Computer Science(), vol 2313. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46016-0_30

Download citation

  • DOI: https://doi.org/10.1007/3-540-46016-0_30

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43475-7

  • Online ISBN: 978-3-540-46016-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation