A nonparallel support vector machine for a classification problem with universum learning

• Autores: Zhiquan Qi, Yingjie Tian, Yong Shi
• Localización: Journal of computational and applied mathematics, ISSN 0377-0427, Vol. 263, Nº 1, 2014, págs. 288-298
• Idioma: inglés
• DOI: 10.1016/j.cam.2013.11.003
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• Resumen

  • Universum samples, defined as samples not belonging to any class for a classification problem of interest, have been useful in supervised learning. Here we design a new nonparallel support vector machine (U-NSVM) that can exploit prior knowledge embedded in the universum to construct a more robust classifier for training. To this end, U-NSVM maximizes the two margins associated with the two closest neighboring classes, which is combined by two nonparallel hyperplanes. Therefore, U-NSVM has better flexibility and can yield a more reasonable classifier in most cases. In addition, our method includes fewer parameters than U-SVM, so is easier to implement. Experiments demonstrate that U-NSVM outperforms the traditional SVM and U-SVM.