A Maximal Figure-of-Merit (MFoM)-learning approach to robust classifier design for text categorization

ACM Transactions on Information Systems

Volumn 24, Issue 2, 2006, Pages 190-218

  Gao, Sheng ^a   Wen, W U ^b,e   Lee, Chin Hui ^c   Chua, Tat Seng ^d  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^d NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^e Carnegie Mellon University   (United States)

Author keywords

Decision tree; Generalized probabilistic descent method; Information retrieval; Latent semantic indexing; Maximal figure of merit; Text categorization

Indexed keywords

DECISION TREE; GENERALIZED PROBABILISTIC DESCENT METHOD; LATENT SEMANTIC INDEXING; MAXIMAL FIGURE-OF-MERIT; TEXT CATEGORIZATION;

DATA STRUCTURES; DECISION SUPPORT SYSTEMS; INFORMATION RETRIEVAL; LEARNING SYSTEMS; PROBABILISTIC LOGICS; SEMANTICS;

CLASSIFIERS;

EID: 33746802824     PISSN: 10468188     EISSN: 10468188     Source Type: Journal    
DOI: 10.1145/1148020.1148022     Document Type: Article

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