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Abstract: Anomalous data lead to scientific discoveries. Although machine learning systems can be forced to resolve anomalous data, these systems use general learning algorithms to do so. To determine whether anomaly-driven approaches to discovery produce more accurate models than the standard approaches, we built a program called Kalpana. We also used Kalpana to explore means for identifying those anomaly resolutions that are acceptable to domain experts. Our experiments indicated that anomaly-driven... (Update)
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BibTeX entry: (Update)
@misc{ approach-science,
author = "Will Bridewell",
title = "Science As An Anomaly-Driven Enterprise",
url = "citeseer.ist.psu.edu/bridewell04science.html" }
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Documents on the same site (http://cll.stanford.edu/~willb/publications.php): More
Reducing Overfitting in Process Model Induction - Bridewell, Asadi, Langley.. (2005) (Correct)
Science as an Anomaly-Driven Enterprise - Bridewell (2004) (Correct)
Extracting Plausible Explanations of Anomalous Data - Will Bridewell And (Correct)
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