Theodore Kalamboukis

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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 report on the experiments conducted by the IPL team within the context of the ImageCLEF 2014 challenge on Scalable Concept Image Annotation. Our approach encompasses, a CBIR phase following with a concept extraction procedure. The content based retrieval utilizes Latent Semantic Analysis on a set of multiple Compact Composite Features to(More)
The wide availability and accessibility of information have made its management and deployment even more difficult. To this end, remarkable effort has been made for the development of information systems that handle the processing, analysis and management of information. However, the success of these systems does not only depend on the quality of(More)
This article presents an experimental evaluation on using a refined approach to the Latent Semantic Analysis (LSA) for efficiently searching very large image databases. It also describes IPL’s participation to the image CLEF ad-hoc textual and visual retrieval as well as modality classification for the Medical Task in 2013. We report on our approaches and(More)