Maxi–Min Margin Machine: Learning Large Margin Classifiers Locally and Globally

@article{Huang2008MaxiMinMM,
  title={Maxi–Min Margin Machine: Learning Large Margin Classifiers Locally and Globally},
  author={Kaizhu Huang and Haiqin Yang and Irwin King and Michael R. Lyu},
  journal={IEEE Transactions on Neural Networks},
  year={2008},
  volume={19},
  pages={260-272}
}
In this paper, we propose a novel large margin classifier, called the maxi-min margin machine (M4). This model learns the decision boundary both locally and globally. In comparison, other large margin classifiers construct separating hyperplanes only either locally or globally. For example, a state-of-the-art large margin classifier, the support vector machine (SVM), considers data only locally, while another significant model, the minimax probability machine (MPM), focuses on building the… CONTINUE READING
34 Extracted Citations
24 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 34 extracted citations

Referenced Papers

Publications referenced by this paper.

Similar Papers

Loading similar papers…