Prototype Learning with Attributed Relational Graphs

  • Pasquale Foggia
  • Roberto Genna
  • Mario Vento
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)


An algorithm for learning structural patterns given in terms of Attributed Relational Graphs (ARG’s) is presented. The algorithm, based on inductive learning methodologies, produces general and coherent prototypes in terms of Generalized Attributed Relational Graphs (GARG’s), which can be easily interpreted and manipulated. The learning process is defined in terms of inference operations especially devised for ARG’s, as graph generalization and graph specialization, making so possible the reduction of both the computational cost and the memory requirement of the learning process. Experimental results are presented and discussed with reference to a structural method for recognizing characters extracted from ETL database.


Graph Match Heuristic Function Edge Attribute Graph Match Algorithm Inference Operation 
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

  • Pasquale Foggia
    • 1
  • Roberto Genna
    • 1
  • Mario Vento
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaUniversità di Napoli “Federico II”NapoliItaly

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