Reliable learning: a theoretical framework

  • Authors:
  • Marco Muselli;Francesca Ruffino

  • Affiliations:
  • Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, Genova, Italy;Dipartimento di Scienze dell'Informazione, Università di Milano, Milano, Italy

  • Venue:
  • KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
  • Year:
  • 2007

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Abstract

A proper theoretical framework, called reliable learning, for the analysis of consistency of learning techniques incorporating prior knowledge for the solution of pattern recognition problems is introduced by properly extending standard concepts of Statistical Learning Theory. In particular, two different situations are considered: in the first one a reliable region is determined where the correct classification is known; in the second case the prior knowledge regards the correct classification of some points in the training set. In both situations sufficient conditions for ensuring the consistency of the Empirical Risk Minimization (ERM) criterion is established and an explicit bound for the generalization error is derived.