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Published in:
2007 | OriginalPaper | Chapter
Reliable Learning: A Theoretical Framework
Authors : Marco Muselli, Francesca Ruffino
Published in: Knowledge-Based Intelligent Information and Engineering Systems
Publisher: Springer Berlin Heidelberg
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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.