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The paper deals with the text classification problem where labeled training samples are very limited while unlabeled data are readily available in large quantities. The paper proposes an efficient classification algorithm that incorporates a weighted k-means clustering scheme into an Expectation Maximization (EM) process. It aims to balance predictive values between labeled and unlabeled training...
We introduce a new method for dimensionality reduction by attribute extraction and evaluate its impact on text classification. The textual contents in body sections of the news in Reuters-21758 are the selected attributes for classification. Using the offered method, high dimension of attributes- words extracted from the news bodies- are projected onto a new hyper plane having dimensions equal to...
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