| Paper: | MLSP-P2.3 | ||
| Session: | Learning Theory and Modeling | ||
| Time: | Tuesday, May 16, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Learning Theory and Modeling | ||
| Title: | FUZZY INTEGRAL-BASED MIXTURE TO SPEED UP THE ONE-AGAINST-ALL MULTICLASS SVM | ||
| Authors: | Hassiba Nemmour, Youcef Chibani, USTHB, Algeria | ||
| Abstract: | The One-Against-All (OAA) is the most widely used implementation of multiclass SVM. For a K-class problem, it performs K binary SVM designed to separate a class from all the others. All SVM are performed over the full database which is, however, a time-consuming task especially for large scale problems. To overcome this limitation, we propose a mixture scheme to speed-up the training of OAA. Thus, each binary problem is divided into a set of sub-problems trained by different SVM modules whose outputs are subsequently combined throughout a gating network. This mixture is based on Sugeno’s fuzzy integral in which the gater is expressed by fuzzy measures. Experiments were conducted on two benchmark databases which concern Handwritten Digit Recognition (ODR) and Face Recognition (FR). The results indicate that the proposed scheme allows a significant training and recognition time improvement. | ||
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