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#One versus rest synonym software
Software available at (2001)Ĭhen, D.R., Wu, Q., Ying, Y., Zhou, D.X.: Support vector machine soft margin classifiers: error analysis. 1, 113–141 (2001)Ĭhang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. This process is experimental and the keywords may be updated as the learning algorithm improves.Īllwein, E., Schapire, R., Singer, Y.: Reducing multiclass to binary: a unifying approach for margin classifiers. These keywords were added by machine and not by the authors. Moreover, the proposed method turns out to be much more efficient than the original one-versus-one strategy. The results show that we obtain better recognition ratio on all tested databases. We used SVM classifier on several different databases to test our solution. As the performance we understand both recognition ratio and the speed of the proposed algorithm. In this paper, we try to combine theses strategies to exploit their strong aspects to achieve better performance. There are several papers dealing with these methods, improving and comparing them. Two of the widely known are one-versus-one and one-versus-rest strategies. There are many methods of decomposition such a task into the set of smaller classification problems involving two classes only. Support Vector Machine (SVM) is a binary classifier, but most of the problems we find in the real-life applications are multiclass.
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