3 papers
cs.LG2019
Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization
Wanli Shi, Bin Gu, Xinag Li +1
Semi-supervised ordinal regression (SOR) problems are ubiquitous in real-world applications, where only a few ordered instances are labeled and massive instances remain unlabel…
cs.LG2019
Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization
Wanli Shi, Bin Gu, Xiang Li +2
Semi-supervised learning is pervasive in real-world applications, where only a few labeled data are available and large amounts of instances remain unlabeled. Since AUC is an impor…
cs.LG2019
Scalable Semi-Supervised SVM via Triply Stochastic Gradients
Xiang Geng, Bin Gu, Xiang Li +3
Semi-supervised learning (SSL) plays an increasingly important role in the big data era because a large number of unlabeled samples can be used effectively to improve the performan…