Combining O(log K) binary classifiers to solve K-class problems has fundamental limits that depend on data dimensionality and noise—understanding these limits helps design efficient distributed classification systems.
This paper studies how to build a multiclass classifier by combining many simple binary classifiers in a distributed way. The authors analyze the fundamental performance limits when using hyperplane-based binary classifiers on Gaussian data, deriving theoretical bounds across different scenarios and validating them with experiments.