Francesco Tortorella

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This paper presents a novel reject rule for support vector classifiers, based on the receiver operating characteristic (ROC) curve. The rule minimises the expected classification cost, defined on the basis of classification and the error costs for the particular application at hand. The rationale of the proposed approach is that the ROC curve of the SVM(More)
In this paper we propose a reject rule applicable to a Multi-Expert System (MES). The rule is adaptive to the given domain and allows the achievement of the best trade-off between reject and error rates as a function of the costs attributed to errors and rejects in the considered application. The results of the method are particularly effective since the(More)
A graph matching algorithm is illustrated and its performance compared with that of a well known algorithm performing the same task. According to the proposed algorithm, the matching process is carried out by using a State Space Representation: a state represents a partial solution of the matching between two graphs, and a transition between states(More)
In the present paper we propose a method for determining the best trade-o! between error rate and reject rate for a multi-expert system (MES) using the Bayesian combining rule. The method is based on the estimation of the reliability of each classi"cation act and on the evaluation of the convenience of rejecting the input sample when the reliability is(More)
Criteria for evaluating the classification reliability of a neural classifier and for accordingly making a reject option are proposed. Such an option, implemented by means of two rules which can be applied independently of topology, size, and training algorithms of the neural classifier, allows one to improve the classification reliability. It is assumed(More)