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arXiv · 1908.04470

Comparison theorems on large-margin learning

Jun Fan·Dao-Hong Xiang
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Abstract

This paper studies binary classification problem associated with a family of loss functions called large-margin unified machines (LUM), which offers a natural bridge between distribution-based likelihood approaches and margin-based approaches. It also can overcome the so-called data piling issue of support vector machine in the high-dimension and low-sample size setting. In this paper we establish some new comparison theorems for all LUM loss functions which play a key role in the further error analysis of large-margin learning algorithms.

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Jun Fan, Dao-Hong Xiang. 2019-08-13. Comparison theorems on large-margin learning. https://arxiv.org/abs/1908.04470

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