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Abstract: Most learning algorithms work most effectively when their training data contain completely specified labeled samples. In many diagnostic tasks, however, the data will include the values of only some of the attributes; we model this as a blocking process that hides the values of those attributes from the learner. While blockers that remove the values of critical attributes can handicap a learner, this paper instead focuses on blockers that remove only irrelevant attribute values, i.e., values... (Update)
Context of citations to this paper: More
.... be available, such as the assumption (if true) that each training example includes only the information required to classify that instance [GGK97]. The appendix supplies the relevant proofs. We close this section by describing related research. Related Results: Our underlying...
...those systems, however, assume that the values of all variables, both relevant and irrelevant, are given. The Greiner et al. [GGK97] model is similar, but here the learner sees only the values of the relevant variables. To connect this to our model, note that an active...
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BibTeX entry: (Update)
Russell Greiner, Adam Grove, and Alex Kogan. Knowing what doesn't matter: Exploiting the omission of irrelevant data. Artificial Intelligence, December 1997. http://www.cs.ualberta.ca/greiner/PAPERS/superfluous-journal.ps. http://citeseer.ist.psu.edu/article/greiner94knowing.html More
@article{ greiner97knowing,
author = "Russell Greiner and Adam J. Grove and Alexander Kogan",
title = "Knowing what doesn't Matter: Exploiting the Omission of Irrelevant Data",
journal = "Artificial Intelligence",
volume = "97",
number = "1-2",
pages = "345-380",
year = "1997",
url = "citeseer.ist.psu.edu/article/greiner94knowing.html" }
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