Dynamic Reconfiguration of Feature Models: an Algorithm and its Evaluation

Le titulaire du droit d'exercer un recours contre un jugement prononçant la dissolution d'une société commerciale, note sous Bruxelles (9ème ch.) 25 avril 2002.. Delvaux,

We then perform our Optimal Feature Weighting algorithm with the new set of features obtained by the Fisher preselection using for A a support vector machine with linear kernel..

From the research conducted it can be concluded that the proposed method is a robust Discernibility metric, yielding a very high correlation with the average Accuracy Rate over

Unsupervised feature selection is mostly assessed along a supervised learning setting, depending on whether the selected features ef- ficiently permit to predict the (unknown)

Instead of using the existing aggregation methods which have several issues, we adapted the weighted median so that it can be used for computing feature based product

This papers introduces a novel conservative feature subset selection method with informatively missing data, i.e., when data is not missing at random but due to an unknown

The article is structured in the following manner: in the section 2, we present briefly some basic theoretical aspects concerning the discriminant analysis; in the section 3 we

Experimental results on the problems of emotion classification in speech and music have shown that selecting relevant features improves the classification accuracy, and for this