Lp-norm Sauer-Shelah lemma for margin multi-category classifiers

@article{Guermeur2017LpnormSL,
  title={Lp-norm Sauer-Shelah lemma for margin multi-category classifiers},
  author={Yann Guermeur},
  journal={J. Comput. Syst. Sci.},
  year={2017},
  volume={89},
  pages={450-473}
}
In the framework of agnostic learning, one of the main open problems of the theory of multi-category pattern classification is the characterization of the way the complexity varies with the number C of categories. More precisely, if the classifier is characterized only through minimal learnability hypotheses, then the optimal dependency on C that an upper bound on the probability of error should exhibit is unknown. We consider margin classifiers. They are based on classes of vector-valued… CONTINUE READING

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