Learning from data with bounded inconsistency: theoretical and experimental results
Haym Hirsh, William W. Cohen · 1994
This paper presents an approach to concept learning from inconsistent data that foregoes a solution to the full-blown problem and instead considers a subcase, called bounded inconsistency. Data are said to have bounded inconsistency when some small perturbation to the description of any bad instance will result in a good instance.