Tiziano Fagni

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This article discusses efficiency and effectiveness issues in caching the results of queries submitted to a Web search engine (WSE). We propose SDC (Static Dynamic Cache), a new caching strategy aimed to efficiently exploit the temporal and spatial locality present in the stream of processed queries. SDC extracts from historical usage data the results of(More)
Hierarchical text categorization (HTC) approaches have recently attracted a lot of interest on the part of researchers in human language technology and machine learning, since they have been shown to bring about equal, if not better, classification accuracy with respect to their “flat” counterparts while allowing exponential time savings at both learning(More)
Hierarchical Text Categorization (HTC) is the task of generating (usually by means of supervised learning algorithms) text classifiers that operate on hierarchically structured classification schemes. Notwithstanding the fact that most large-sized classification schemes for text have a hierarchical structure, so far the attention of text classification(More)
Hierarchical text classification (HTC) approaches have recently attracted a lot of interest on the part of researchers in human language technology andmachine learning, since they have been shown to bring about equal, if not better, classification accuracywith respect to their “flat” counterparts while allowing exponential time savings at both learning and(More)
AdaBoost.MH is a popular supervised learning algorithm for building multi-label (aka n-of-m) text classifiers. AdaBoost.MH belongs to the family of “boosting” algorithms, and works by iteratively building a committee of “decision stump” classifiers, where each such classifier is trained to especially concentrate on the document-class pairs that previously(More)
Hierarchical Text Categorization (HTC) is the task of generating (usually by means of supervised learning algorithms) text classifiers that operate on hierarchically structured classification schemes. Notwithstanding the fact that most largesized classification schemes for text have a hierarchical structure, so far the attention of text classification(More)
In this paper we tackle the problem of image search when the query is a short textual description of the image the user is looking for. We choose to implement the actual search process as a similarity search in a visual feature space, by learning to translate a textual query into a visual representation. Searching in the visual feature space has the(More)
JaTeCS is an open source Java library that supports research on automatic text categorization and other related problems, such as ordinal regression and quantification, which are of special interest in opinion mining applications. It covers all the steps of an experimental activity, from reading the corpus to the evaluation of the experimental results. As(More)
We present a system for image classification based on an adaptive committee of five classifiers, each specialized on classifying images based on a single MPEG-7 feature. We test four different ways to set up such a committee, and obtain important accuracy improvements with respect to a baseline in which a single classifier, working an all five features at(More)