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This paper addresses the problem of learning to classify textsby exploiting information derived from clustering both training and testing sets. The incorporation of knowledge resulting from clustering into the feature space representation of the texts is expected to boost the performance of a classifier. Experiments conducted on several widely used datasets(More)
This paper addresses the problem of learning to classify texts by exploiting information derived from both training and testing sets. To accomplish this, clustering is used as a complementary step to text classification , and is applied not only to the training set but also to the testing set. This approach allows us to study the location of the testing(More)
A novel fast and accurate supervised learning algorithm is proposed as a general text classification algorithm for linearly separated data. The strategy of the algorithm takes advantage of the training errors to successively refine an initial classifier. Experimental evaluation of the proposed algorithm on standard text collections, show that results(More)
In this article we have assembled the experience obtained from our participation in the imageCLEF evaluation task over the past two years. Exploitation on the use of linear combinations for image retrieval has been attempted by combining visual and textual sources of images. From our experiments we conclude that a mixed retrieval technique that applies both(More)