36 citations · 38 across the 4 of their papers we have counts for
4 papers
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active Learning
Kevin Miller, Jeff Calder
We show that uncertainty sampling is sufficient to achieve exploration versus exploitation in graph-based active learning, as long as the measure of uncertainty properly aligns wit…
Graph-based Active Learning for Semi-supervised Classification of SAR Data
Kevin Miller, John Mauro, Jason Setiadi +4
We present a novel method for classification of Synthetic Aperture Radar (SAR) data by combining ideas from graph-based learning and neural network methods within an active learnin…
Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations
Kevin Miller, Hao Li, Andrea L. Bertozzi
We present a novel adaptation of active learning to graph-based semi-supervised learning (SSL) under non-Gaussian Bayesian models. We present an approximation of non-Gaussian distr…
Forward Thinking: Building Deep Random Forests
Kevin Miller, Chris Hettinger, Jeffrey Humpherys +2
The success of deep neural networks has inspired many to wonder whether other learners could benefit from deep, layered architectures. We present a general framework called forward…