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Abstract: One common approach to using a prior domain theory as a learning bias is to revise the theory in accordance with a set of training examples. More recently, another class of methods has arisen in which the theory is reinterpreted, either by probabilizing it, or by using its components in constructive induction. Revision-based methods tend to work best when flaws in the given theory are localized, whereas reinterpretation methods tend to work well when flaws are distributed evenly throughout the... (Update)
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BibTeX entry: (Update)
@inproceedings{ engelson96identifying,
author = "Sean P. Engelson and Moshe Koppel",
title = "Identifying the Information Contained in a Flawed Theory",
booktitle = "International Conference on Machine Learning",
pages = "131-138",
year = "1996",
url = "citeseer.ist.psu.edu/14825.html" }
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