Supervised training of a neural network for classification via successive modification of the training data - an experimental study

  • Mayer Aladjem
3 Machine Learning Learning Advances in Neural Networks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1416)


A method for training of an ML network for classification has been proposed by us in [3,4]. It searches for the non-linear discriminant functions corresponding to several small local minima of the objective function. This paper presents a comparative study of our method and conventional training with random initialization of the weights. Experiments with a synthetic data set and the data set of an OCR problem are discussed. The results obtained confirm the efficacy of our method which finds solutions with lower misclassification errors than does conventional training.


Neural networks for classification auto-associative network projection pursuit structure removal discriminant analysis statistical pattern recognition 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    M.E. Aladjem, “Linear discriminant analysis for two classes via removal of classification structure”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, pp. 187–192, 1997.Google Scholar
  2. 2.
    M.E. Aladjem, “Nonparametric discriminant analysis via recursive optimization of Patrick-Fisher distance”, IEEE Trans. on Syst., Man, Cybern., vol.28B, No 1, 1998.Google Scholar
  3. 3.
    M.E. Aladjem, “ Recursive training of an ML neural network for pattern recognition”, IEEE Trans. on Neural Networks (submitted).Google Scholar
  4. 4.
    M.E. Aladjem, “Training of an ML neural network for classification via recursive reduction of the class separation”, 14th Int. Conf. on Pattern Recognition, Brisbane, Australia, 17–20 August, 1998 (submitted).Google Scholar
  5. 5.
    C.M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press Inc., New York, 1995.Google Scholar
  6. 6.
    J.H. Friedman, “Exploratory projection pursuit”, Journal of the American Statistical Association, vol. 82,pp. 249–266, 1987.Google Scholar
  7. 7.
    G.J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition, John Wiley & Sons, Inc., New York, 1992.Google Scholar
  8. 8.
    W.N.Venables and B.D.Ripley, Modern Applied Statistics with S-Plus, Springer-Verlag, New York, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Mayer Aladjem
    • 1
  1. 1.Department of Electrical and Computer EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael

Personalised recommendations