Papers by Donato Barbuzzi
2012 International Conference on Frontiers in Handwriting Recognition, 2012
ABSTRACT Classifier combination is a powerful paradigm to deal with difficult pattern classificat... more ABSTRACT Classifier combination is a powerful paradigm to deal with difficult pattern classification problems. As matter of this fact, multi-classifier systems have been widely adopted in many applications for which very high classification performance is necessary. Notwithstanding, multi-classifier system design is still an open problem. In fact, complexity of multi-classifiers systems make the theoretical evaluation of system performance very difficult and, consequently, also the design of a multi-classifier system. This paper presents a new approach for the design of a multi-classifier system. In particular, the problem of feature selection for a multi-classifier system is addressed and a genetic algorithm is proposed for automatic selecting the optimal set of features for each individual classifier of the multi-classifier system. The experimental results, carried out in the field of handwritten digit recognition, demonstrate the effectiveness of the proposed approach.
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Lecture Notes in Computer Science, 2013
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International Journal of Signal and Imaging Systems Engineering, 2014
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2013 12th International Conference on Document Analysis and Recognition, 2013
ABSTRACT The aim of this paper is to explore the properties of a new zoning technique based on Vo... more ABSTRACT The aim of this paper is to explore the properties of a new zoning technique based on Voronoi tessellation for the task of handwritten digit recognition. This technique extracts features according to an optimal zoning distribution, obtained by an evolutionary-strategy based search. Extensive experiments have been conducted on the MNIST dataset to investigate strengths and weakness of the proposed approach. Comparisons with regular square zoning reveal that the presented zoning strategy achieves better results with any type of features. Furthermore, the proposed zoning method, jointly with a suitable choice of features, allows a low complexity classifier to reach excellent performances both in terms of accuracy and speed.
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2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), 2012
ABSTRACT This paper presents three strategies in order to re-train classifiers in a multi-expert ... more ABSTRACT This paper presents three strategies in order to re-train classifiers in a multi-expert scenario when new labeled data become available. The simplest possibility is the use of the entire new dataset. The second possibility is related to the consideration that each single classifier is able to select new patterns starting from those on which it performs a miss-classification. Finally, the multi expert system behavior can be inspected to select profitable samples. More specifically a misclassified sample, for a particular classifier, is used to update that classifier only if it produces a miss-classification by the ensemble of classifiers. The three approaches are compared under different conditions on two different state of the art performing classifiers by considering the CEDAR (handwritten digit) database. It is shown how results depend by the amount of the new training samples, as well as by the specific combination decision schema.
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Papers by Donato Barbuzzi