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Improving Pattern Recognition Using Several Feature Vectors

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MICAI 2002: Advances in Artificial Intelligence (MICAI 2002)

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

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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.

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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
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  2. Juan Humberto Sossa Azuela
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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

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© 2002 Springer-Verlag Berlin Heidelberg

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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

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

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43475-7

  • Online ISBN: 978-3-540-46016-9

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