标题： 基于间隔追求的分类 显示英文标题

标题： Classification using margin pursuit

摘要： 在本工作中，我们研究了一种新的方法来优化二分类器实现的边缘分布。 解决这个问题的经典方法是简单地最大化期望边缘，而最近的提案则考虑了同时控制方差和基于鲁棒位置估计的代理目标，旨在使边缘分布集中在有利区域。 虽然概念上有吸引力，但这些新方法通常计算上不太方便，理论保证有限。 鉴于此，我们提出了一种算法，该算法以一种方式搜索假设空间，使得预设的“边缘水平”最终成为边缘位置的分布稳健估计量。 该过程可以使用梯度下降轻松实现，并在无界输入下接受过风险的有限样本界限。 在真实世界基准数据上的实验验证了理论上强调的基本原理，并暗示了一种有前途的分类新技术。 显示英文摘要

摘要： In this work, we study a new approach to optimizing the margin distribution realized by binary classifiers. The classical approach to this problem is simply maximization of the expected margin, while more recent proposals consider simultaneous variance control and proxy objectives based on robust location estimates, in the vein of keeping the margin distribution sharply concentrated in a desirable region. While conceptually appealing, these new approaches are often computationally unwieldy, and theoretical guarantees are limited. Given this context, we propose an algorithm which searches the hypothesis space in such a way that a pre-set "margin level" ends up being a distribution-robust estimator of the margin location. This procedure is easily implemented using gradient descent, and admits finite-sample bounds on the excess risk under unbounded inputs. Empirical tests on real-world benchmark data reinforce the basic principles highlighted by the theory, and are suggestive of a promising new technique for classification.