Abstract
In this paper, we propose a novel algorithm for multi-class classification, called as Multi-class Twin Support Vector Machine (MTWSVM) which is an extension of the binary Twin Support Vector Machine (TWSVM). MTWSVM is based on “one-against-one” strategy in which the patterns of each class are trained with the patterns of another class. To speed up the training phase, optimization problems are solved by Successive Over Relaxation (SOR) technique. The experiment is performed on eight benchmark datasets and the performance of the proposed approach is compared with the existing multi-class approaches based on Support Vector Machines and Twin Support Vector Machines.
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Tomar, D., Agarwal, S. (2016). Multi-class Twin Support Vector Machine for Pattern Classification. In: Nagar, A., Mohapatra, D., Chaki, N. (eds) Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Smart Innovation, Systems and Technologies, vol 43. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2538-6_11
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DOI: https://doi.org/10.1007/978-81-322-2538-6_11
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