Abstract
Systems interacting with the real world must address the issues raised by the possible presence of errors in the observations. In this paper we first present a framework for discussing imperfect data and the resulting problems. We distinguish between various categories of errors, such as random errors, systematic errors or errors in teaching. We examine some of the techniques currently used in AI for dealing with random errors and discuss the way the other types of errors could be dealt with.
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Brazdil, P., Clark, P. (1990). Learning from Imperfect Data. In: Brazdil, P.B., Konolige, K. (eds) Machine Learning, Meta-Reasoning and Logics. The Kluwer International Series in Engineering and Computer Science, vol 82. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1641-1_10
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DOI: https://doi.org/10.1007/978-1-4613-1641-1_10
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