A maximal figure-of-merit (MFoM)-learning approach to robust classifier design for text categorization

doi:10.1145/1148020.1148022

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A maximal figure-of-merit (MFoM)-learning approach to robust classifier design for text categorization

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Authors:	Sheng Gao	Institute for Infocomm Research, Singapore
	Wen Wu	Carnegie Mellon University, Pittsburgh, PA
	Chin-Hui Lee	Georgia Institute of Technology, Atlanta, GA
	Tat-Seng Chua	National University of Singapore, Singapore

	2006 Article
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ACM Transactions on Information Systems (TOIS) TOIS Homepage archive

Volume 24 Issue 2, April 2006
ACM New York, NY, USA
table of contents doi>10.1145/1148020.1148022

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Tags: algorithms decision tree design methodology experimentation generalized probabilistic descent method information retrieval information search and retrieval latent semantic indexing maximal figure-of-merit performance text categorization theory

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