PLUME: Polyhedral Learning Using Mixture of Experts
On the map: Mixture-of-experts → MoE theory and gating functions
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in MoE theory and gating functions, one of 12 directions in Mixture-of-experts. Builds on 1 of its 22 references that are in the map. First posted on arXiv 22 Apr 2019.
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1 of 22 references.
References · 1
- 2016A vector-contraction inequality for Rademacher complexitiescited by 141
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