Boîte à outils SVM simple et rapide
If SVM (Support Vector Machines) are now considered as one of the best learning methods, they are still considered as slow. Here we propose a Matlab toolbox that enables the usage of SVM in a fast and simple way. This is done thanks to the projected gradient method which is well adapted to the problem : SimpleSVM (VIS 03). We chose to implement this algorithm with Matlab environment since it is user-friendly and efficient - it uses the ATLAS (Automatically Tuned Linear Algebra Software) library. The comparison to the state of the art in this field, SMO (Sequential Minimal Optimization) shows that in some cases, our solution is faster and less complex. In order to point out how fast and simple our method is, we give here results on the MNITS database. It was possible to compute a satisfying solution in a quite short time (one hour and a half on a PC with Linux distribution to compute 45 binary classifiers, with