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Experimental Guidelines for Semantic-Based Regularization

  • Claudio SaccàEmail author
  • Michelangelo Diligenti
  • Marco Gori
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 26)

Abstract

This paper presents a novel approach for learning with constraints called Semantic-Based Regularization. This paper shows how prior knowledge in form of First Order Logic (FOL) clauses, converted into a set of continuous constraints and integrated into a learning framework, allows to jointly learn from examples and semantic knowledge. A series of experiments on artificial learning tasks and application of text categorization in relational context will be presented to emphasize the benefits given by the introduction of logic rules into the learning process.

Keywords

Text Categorization First Order Logic Logic Rule Logic Knowledge Kernel Machine 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Claudio Saccà
    • 1
    Email author
  • Michelangelo Diligenti
    • 1
  • Marco Gori
    • 1
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversità di SienaSienaItaly

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