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Data Mining and Knowledge Discovery by Means of Monotone Boolean Functions

  • Evangelos Triantaphyllou
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 43)

Abstract

In all previous discussions the problem was how to infer a general Boolean function based on some training examples. Such a Boolean function can be completely inferred if all possible binary examples (states) in the space of the attributes are used for training. Thus, one may never be 100% certain about the validity of the inferred knowledge when the number of training examples is less than 2 n . The situation is different, however, if one deals with the inference of systems that exhibit monotonic behavior. The developments presented in this chapter are based on the award-winning doctoral work of Vetle I. Torvik and in particular on the research results first published in [ Torvik and Triantaphyllou, 2002; 2003; 2004; 2006].

Keywords

Boolean Function Query Complexity Conjunctive Normal Form Inference Algorithm Inference Process 
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 Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Computer ScienceLouisiana State UniversityBaton RougeUSA

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