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Combining Labeled and Unlabeled Data for MultiClass Text Categorization

Overview Supervised learning techniques for text classification often require a large number of labeled examples to learn accurately. Current text learning techniques for combining labeled and unlabeled, such as EM and Co-Training are mostly applicable for classification tasks with a small number of classes and do not scale up well for large multiclass problems. In this paper, Accenture develop a framework to incorporate unlabeled data in the Error-Correcting Output Coding (ECOC) setup by first decomposing multiclass problems into multiple binary problems and then using Co-Training to learn the individual binary classification problems.

Further White Paper Details
Publisher	Accenture	File Format	PDF, requires Acrobat Rdr 5
Date Published	April 2002	Downloads	94
Format	White Papers
Topics	Methodology, Engineering / R&D

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