A new maximal-margin spherical-structured multi-class support vector machine

@article{Hao2007ANM,
  title={A new maximal-margin spherical-structured multi-class support vector machine},
  author={Pei-Yi Hao and Jung-Hsien Chiang and Yen-Hsiu Lin},
  journal={Applied Intelligence},
  year={2007},
  volume={30},
  pages={98-111}
}
Support vector machines (SVMs), initially proposed for two-class classification problems, have been very successful in pattern recognition problems. For multi-class classification problems, the standard hyperplane-based SVMs are made by constructing and combining several maximal-margin hyperplanes, and each class of data is confined into a certain area constructed by those hyperplanes. Instead of using hyperplanes, hyperspheres that tightly enclosed the data of each class can be used. Since the… CONTINUE READING
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