Soufiane El Jelali

Learn More
Standard learning procedures are better fitted to estimation than to classification problems, and focusing the training on appropriate samples provides performance advantages in classification tasks. In this paper, we combine these ideas creating smooth targets for classification by means of a convex combination of the original target and the output of an(More)
—When training machines classifiers, it is possible to replace hard classification targets by their emphasized soft versions so as to reduce the negative effects of using cost functions as approximations to misclassification rates. This emphasis has the same effect as sample editing methods which have proved to be effective for improving classifiers(More)
In a relational database, data are stored in primary and secondary tables. Propositionalization can transform a relational database into a single attribute-value table, and hence becomes a useful technique for mining relational databases. However, most of the existing propositionalization approaches deal with categorical attributes, and cannot handle a(More)
The perspective of online dispute resolution (ODR) is to develop an online electronic system aimed at solving out-of-court disputes. Among ODR schemes, eMediation is becoming an important tool for encouraging the positive settlement of an agreement among litigants. The main motivation underlying the adoption of eMediation is the time/cost reduction for the(More)
Replacing a hard decision by a soft targets version including an attentional mechanism provides performance advantage and flexibility to solve classification tasks. In this paper, we modify the standard emphasized soft target method by proposing two new ideas, to avoid unnecessary updating and inappropriate definition of soft targets, in order to increase(More)
—In this paper, a nonlinear system identification based on support vector machines (SVM) has been addressed. A family of SVM-ARMA models is presented in order to integrate the input and the output in the reproducing kernel Hilbert space (RKHS). The performances of the different SVM-ARMA formulations for system identification are illustrated with two systems(More)
  • 1