这篇论文告诉你用少数几个二分类器拼多分类器到底能做到多好，有理论保证，做分布式分类的值得看看。
该论文研究了如何组合O(log K)个简单二元分类器（超平面）来构建K类分类器。在K个类中心为独立高斯点、观测受高斯噪声污染的设定下，推导了不同解码和维度区间的显式性能界限。大量仿真实验验证了理论结果的有效性。
Fundamental limits of distributed multiclass classification from simple binary decisions
We consider the problem of constructing a $K$-class classifier from the combination of $O(\log K)$ simple binary classifiers -- this is a natural paradigm to construct a sophisticated classifier in a distributed manner with each agent performing a relatively straightforward task. We study the fundamental performance limits of such a classifier when the corresponding binary classifiers are hyperplanes. For a stylized Gaussian setting where the $K$ class centers are independent Gaussian points in $\mathbb R^d$ and the observations are corrupted by Gaussian noise, we derive explicit performance bounds across several decoding and dimensional regimes. Extensive simulation experiments provide strong empirical validation of the presented theoretical results.