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Learning Classes Correlated to a Hierarchy

Author(s)
Shih, Lawrence; Karger, David
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Abstract
Trees are a common way of organizing large amounts of information by placing items with similar characteristics near one another in the tree. We introduce a classification problem where a given tree structure gives us information on the best way to label nearby elements. We suggest there are many practical problems that fall under this domain. We propose a way to map the classification problem onto a standard Bayesian inference problem. We also give a fast, specialized inference algorithm that incrementally updates relevant probabilities. We apply this algorithm to web-classification problems and show that our algorithm empirically works well.
Date issued
2003-05-01
URI
http://hdl.handle.net/1721.1/6719
Other identifiers
AIM-2003-013
Series/Report no.
AIM-2003-013

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  • AI Memos (1959 - 2004)

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