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Large Margin Distribution Learning

机译：大保证金分配学习

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摘要

Support vector machines (SVMs) and Boosting are possibly the two most popular learning approaches during the past two decades. It is well known that the margin is a fundamental issue of SVMs, whereas recently the margin theory for Boosting has been defended, establishing a connection between these two mainstream approaches. The recent theoretical results disclosed that the margin distribution rather than a single margin is really crucial for the generalization performance, and suggested to optimize the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. Inspired by this recognition, we advocate the large margin distribution learning, a promising research direction that has exhibited superiority in algorithm designs to traditional large margin learning.
机译：支持向量机（SVM）和Boosting可能是过去二十年来最受欢迎的两种学习方法。众所周知，保证金是SVM的基本问题，而最近Boosting的保证金理论得到了捍卫，在这两种主流方法之间建立了联系。最近的理论结果表明，边距分布而不是单个边距对于泛化性能确实至关重要，并建议通过最大化边距均值和同时最小化边距方差来优化边距分布。受到这种认识的启发，我们提倡大余量分布学习，这是一个有前途的研究方向，在算法设计上已显示出优于传统大余量学习的优势。

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