Learning and Forgetting with Local Information of New Objects

  • Fernando D. Vázquez
  • J. Salvador Sánchez
  • Filiberto Pla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)


The performance of supervised learners depends on the presence of a relatively large labeled sample. This paper proposes an automatic ongoing learning system, which is able to incorporate new knowledge from the experience obtained when classifying new objects and correspondingly, to improve the efficiency of the system. We employ a stochastic rule for classifying and editing, along with a condensing algorithm based on local density to forget superfluous data (and control the sample size). The effectiveness of the algorithm is experimentally evaluated using a number of data sets taken from the UCI Machine Learning Database Repository.


Learning Classification Forgetting Editing Condensing 


  1. 1.
    Barandela, R., Juárez, M.: Ongoing learning for supervised pattern recognition. In: 14th Brazilian Symposium Computer Graphics and Image Processing, pp. 51–58 (2001)Google Scholar
  2. 2.
    Bensaid, A.M., Hall, L.O., Bezdek, J.C., Clarke, L.P.: Partially supervised clustering for image segmentation. Pattern Recognition 29, 859–871 (1996)CrossRefGoogle Scholar
  3. 3.
    Blum, A.: Chawla.: Learning from labelled and unlabeled data using graph mincuts. In: 18th International Conference on Machine Learning, pp. 19–26 (2001)Google Scholar
  4. 4.
    Castelli, V., Cover, T.M.: On the exponential value of labeled samples. Pattern Recognition Letters 16, 105–111 (1995)CrossRefGoogle Scholar
  5. 5.
    Dasarathy, B.V.: Adaptive decision systems with extended learning for deployment in partially exposed environments. Optical Engineering 34, 1269–1280 (1995)CrossRefGoogle Scholar
  6. 6.
    Pascual, D., Pla, F., Sánchez, J.S.: Non Parametric Local Density-based Clustering for Multimodal Overlapping Distributions. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 671–678. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Vázquez, F., Sánchez, J.S., Pla, F.: A stochastic approach to Wilson’s editing algorithm. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, pp. 35–42. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Wilson, D.L.: Asymptotic properties of nearest neighbour rules using edited data. IEEE Trans. on Systems, Man and Cybernetics 2, 408–421 (1972)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Fernando D. Vázquez
    • 1
  • J. Salvador Sánchez
    • 2
  • Filiberto Pla
    • 2
  1. 1.Centro de Reconocimiento de Patrones y Minería de DatosUniversidad de OrienteSantiago de CubaCuba
  2. 2.Dept. Llentguages i Sistemas InformàticsUniversitat Jaume ICastellóSpain

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