论文标题
分类为方向恢复：通过量表不变性提高了保证
Classification as Direction Recovery: Improved Guarantees via Scale Invariance
论文作者
论文摘要
用于二进制分类的现代算法依靠中间回归问题来进行计算障碍。在本文中，我们在分类和回归之间建立了几何区别，该分类与这两种环境中的风险更加精确相关。特别是，我们注意到分类风险仅取决于回归器的方向，我们利用这种规模不变性来改善现有的保证，以确保分类风险如何受中间回归问题中的风险限制。在这些保证的基础上，我们的分析使得可以更准确地相互比较算法，并建议将分类视为回归而不是其副产品的独特之处。虽然回归旨在融合位置中有条件的期望功能，但我们建议分类应旨在恢复其方向。
Modern algorithms for binary classification rely on an intermediate regression problem for computational tractability. In this paper, we establish a geometric distinction between classification and regression that allows risk in these two settings to be more precisely related. In particular, we note that classification risk depends only on the direction of the regressor, and we take advantage of this scale invariance to improve existing guarantees for how classification risk is bounded by the risk in the intermediate regression problem. Building on these guarantees, our analysis makes it possible to compare algorithms more accurately against each other and suggests viewing classification as unique from regression rather than a byproduct of it. While regression aims to converge toward the conditional expectation function in location, we propose that classification should instead aim to recover its direction.
