Abstract
At first, support vector machines (SVMs) were applied to solve binary classification problems. They can also be extended to solve multicategory problems by the combination of binary SVM classifiers. In this paper, we propose a new fuzzy model that includes the advantages of several previously published methods solving their drawbacks. For each datum, a class is rejected using information provided by every decision function related to it. Our proposal yields membership degrees in the unit interval and in some cases, it improves the performance of the former methods in the unclassified regions.
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Puche, J.M., BenÃtez, J.M., Castro, J.L., Mantas, C.J. (2006). Fuzzy Pairwise Multiclass Support Vector Machines. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_53
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DOI: https://doi.org/10.1007/11925231_53
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