Learning Iterative Strategies in Multi-Expert Systems Using SVMs for Digit Recognition

  • Donato Barbuzzi
  • Donato Impedovo
  • Francesco Maurizio Mangini
  • Giuseppe Pirlo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


This paper presents three different learning iterative strategies, in a multi-expert system. In first strategy entire new dataset is used. In second strategy each single classifier selects new samples starting from those on which it performs a misclassification. Finally, the collective behavior of classifiers is studied to select the most profitable samples for knowledge base updating. The experimental results provide a comparison of three approaches under different operating conditions and feedback process. A classifier SVM and four different combination techniques were used by considering the CEDAR (handwritten digit) database. It is shown how results depend by the iterations on the feedback process, as well as by the specific combination decision schema and by data distribution.


Feedback-based strategies Instance Selection Multi Expert Systems 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Donato Barbuzzi
    • 1
  • Donato Impedovo
    • 2
  • Francesco Maurizio Mangini
    • 1
  • Giuseppe Pirlo
    • 1
  1. 1.Department of Computer ScienceUniversity of BariBariItaly
  2. 2.Department of Electrical and Electronic EngineeringPolytechnic of BariBariItaly

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