2 citations · 3 across the 8 of their papers we have counts for
4 papers · 1 filter
A Margin-Maximizing Fine-Grained Ensemble Method
Jinghui Yuan, Hao Chen, Renwei Luo +1
Ensemble learning has achieved remarkable success in machine learning, but its reliance on numerous base learners limits its application in resource-constrained environments. This…
Multi-Task Curriculum Graph Contrastive Learning with Clustering Entropy Guidance
Chusheng Zeng, Bocheng Wang, Jinghui Yuan +2
Recent advances in unsupervised deep graph clustering have been significantly promoted by contrastive learning. Despite the strides, most graph contrastive learning models face cha…
Achieving More with Less: A Tensor-Optimization-Powered Ensemble Method
Jinghui Yuan, Weijin Jiang, Zhe Cao +4
Ensemble learning is a method that leverages weak learners to produce a strong learner. However, obtaining a large number of base learners requires substantial time and computation…
Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping
Jinghui Yuan, Chusheng Zeng, Fangyuan Xie +5
Clustering is a fundamental task in machine learning and data science, and similarity graph-based clustering is an important approach within this domain. Doubly stochastic symmetri…