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Panel Summary Perceptual Learning and Discovering

  • Salvatore Gaglio
  • Floriana Esposito
  • Stefano Nolfi

Abstract

The problem of learning and discovering in perception is addressed and discussed with particular reference to present machine learning paradigms. These paradigms are briefly introduced by S. Gaglio. The subsymbolic approach is addressed by S. Nolfi, and the role of symbolic learning is analysed by F. Esposito. Many of the open problems, that are evidentiated in the course of the panel, show how this is an important field of research that still needs a lot of investigation. In particular, as a result of the whole discussion, it seems that a suitable integration of different approaches must be accurately investigated. It is observed, in fact, that the weakness of the most part of the existing systems is imputed to the existing gap between the rather ideal conditions under which most of those systems are designed to work and the very characteristics of the real world.

Keywords

Unsupervised Learning Perceptual Learning Incremental Learning Input Stimulus Novelty Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • Salvatore Gaglio
    • 1
  • Floriana Esposito
    • 2
  • Stefano Nolfi
    • 3
  1. 1.Dipartimento di Ingegneria ElettricaUniversità di PalermoPalermoItaly
  2. 2.Dipartimento di InformaticaUniversità di BariBariItaly
  3. 3.Istituto di Psicologia del CNRRomaItaly

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