A fast multi-class SVM learning method for huge databases
In this paper, we propose a new learning method for multi-class support vector machines based on single class SVM learning method. Unlike the methods 1vs1 and 1vsR, used in the literature and mainly based on binary SVM method, our method learns a classifier for each class from only its samples and then uses these classifiers to obtain a multiclass decision model. To enhance the accuracy of our method, we build from the obtained hyperplanes new hyperplanes, similar to those of the 1vsR method, for use in classification. Our method represents a considerable improvement in the speed of training and classification as well the decision model size while maintaining the same accuracy as other methods.
Keywords: Support vector machine, Multiclass SVM, One-class SVM, 1vs1, 1vsR
Download Full-Text
ABOUT THE AUTHORS
Djeffal Abdelhamid
Computer science department, LESIA Laboratory, Biskra University, Algeria
Computer science department, LESIA Laboratory, Biskra University, Algeria
Taleb-Ahmed Abdelmalik
LAMIH Laboratory FRE CNRS 3304 UVHC, Valenciennes university, France
Djeffal Abdelhamid
Computer science department, LESIA Laboratory, Biskra University, Algeria
Computer science department, LESIA Laboratory, Biskra University, Algeria
Taleb-Ahmed Abdelmalik
LAMIH Laboratory FRE CNRS 3304 UVHC, Valenciennes university, France
