PLUME: Polyhedral Learning Using Mixture of Experts
Kulin Shah, P. SESHADRI SASTRY, Naresh Manwani · arXiv (Cornell University) · 2019
In this paper, we propose a novel mixture of expert architecture for learning polyhedral classifiers. We learn the parameters of the classifierusing an expectation maximization algorithm. Wederive the generalization bounds of the proposedapproach. Through an extensive simulation study, we show that the proposed method performs comparably to other state-of-the-art approaches.