86 citations · 194 across the 11 of their papers we have counts for
5 papers · 1 filter
Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning
Omar Maddouri, Xiaoning Qian, Francis J. Alexander +2
Classification has been a major task for building intelligent systems as it enables decision-making under uncertainty. Classifier design aims at building models from training data…
Optimal Clustering with Missing Values
Shahin Boluki, Siamak Zamani Dadaneh, Xiaoning Qian +1
Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements. Mis…
Optimal Clustering under Uncertainty
Lori A. Dalton, Marco E. Benalcázar, Edward R. Dougherty
Classical clustering algorithms typically either lack an underlying probability framework to make them predictive or focus on parameter estimation rather than defining and minimizi…
Optimal Bayesian Transfer Learning
Alireza Karbalayghareh, Xiaoning Qian, Edward R. Dougherty
Transfer learning has recently attracted significant research attention, as it simultaneously learns from different source domains, which have plenty of labeled data, and transfers…
Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of Classification Error in the Gaussian Model
Amin Zollanvari, Edward R. Dougherty
The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation…