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An Approach to Guided Learning of Boolean Functions

In: Data Mining and Knowledge Discovery via Logic-Based Methods

Author

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  • Evangelos Triantaphyllou

    (Louisiana State University)

Abstract

In most of the previous treatments it was assumed that somehow we have available two disjoint sets of training data described by binary vectors, that is, the collections of the positive and negative examples. Then the problem was how to infer a Boolean function that “fits these data.” In other words, a Boolean function in CNF or DNF form that satisfies the requirements of the positive and negative examples as described in Chapters 2 and 3. It is hoped at this point that the inferred Boolean function will accurately classify all remaining examples not included in the currently available positive and negative examples.

Suggested Citation

  • Evangelos Triantaphyllou, 2010. "An Approach to Guided Learning of Boolean Functions," Springer Optimization and Its Applications, in: Data Mining and Knowledge Discovery via Logic-Based Methods, chapter 0, pages 101-123, Springer.
    • Handle: RePEc:spr:spochp:978-1-4419-1630-3_5
    DOI: 10.1007/978-1-4419-1630-3_5
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