Learning Classes Correlated to a Hierarchy

dc.creator	Shih, Lawrence
dc.creator	Karger, David
dc.date	2004-10-08T20:38:58Z
dc.date	2004-10-08T20:38:58Z
dc.date	2003-05-01
dc.date.accessioned	2013-10-09T02:46:33Z
dc.date.available	2013-10-09T02:46:33Z
dc.date.issued	2013-10-09
dc.identifier	AIM-2003-013
dc.identifier	http://hdl.handle.net/1721.1/6719
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	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.
dc.format	1146195 bytes
dc.format	480357 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AIM-2003-013
dc.title	Learning Classes Correlated to a Hierarchy

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