Adaptive Graphical Pattern Recognition Beyond Connectionist-Based Approaches

  • Giovanni Adorni
  • Stefano Cagnoni
  • Marco Gori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)

Abstract

This paper proposes a general framework for the development of a novel approach to pattern recognition which is strongly based on graphical data types. These data keep at the same time the highly structured representation of classical syntactic and structural approaches and the subsymbolic capabilities of decision-theoretic approaches, typical of connectionist and statistical models. Like for decision-theoretic models, the recognition ability is mainly gained on the basis of learning from examples, that, however, are strongly structured.

Keywords

Input Graph Connectionist Model Pattern Recognition Method Pattern Recogni Pattern Recognition Problem 
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-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Giovanni Adorni
    • 1
  • Stefano Cagnoni
    • 1
  • Marco Gori
    • 2
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversitá di ParmaItaly
  2. 2.Dipartimento di Ingegneria dell’InformazioneUniversitá di SienaSienaItaly

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