document: A parallel learning algorithm for text classification Text classification is the process of classifying documents into predefined categories based on their content . Existing supervised learning algorithms to automatically classify text need sufficient labeled documents to learn accurately . Applying the Expectation-Maximization ( EM ) algorithm to this problem is an alternative approach that utilizes a large pool of unlabeled documents to augment the available labeled documents . Unfortunately , the time needed to learn with these large unlabeled documents is too high . This paper introduces a novel parallel learning algorithm for text classification task . The parallel algorithm is based on the combination of the EM algorithm and the naive Bayes classifier . Our goal is to improve the computational time in learning and classifying process . We studied the performance of our parallel algorithm on a large Linux PC cluster called PIRUN Cluster . We report both timing and accuracy results . These results indicate that the proposed parallel algorithm is capable of handling large document collections .
doc_bio_tags: O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O