2 citations · 3 across the 2 of their papers we have counts for
5 papers · 1 filter
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance
Marco Loog, Tom Viering
Plotting a learner's generalization performance against the training set size results in a so-called learning curve. This tool, providing insight in the behavior of the learner, is…
Making Learners (More) Monotone
Tom J. Viering, Alexander Mey, Marco Loog
Learning performance can show non-monotonic behavior. That is, more data does not necessarily lead to better models, even on average. We propose three algorithms that take a superv…
Minimizers of the Empirical Risk and Risk Monotonicity
Marco Loog, Tom Viering, Alexander Mey
Plotting a learner's average performance against the number of training samples results in a learning curve. Studying such curves on one or more data sets is a way to get to a bett…
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization
Alexander Mey, Tom Viering, Marco Loog
Manifold regularization is a commonly used technique in semi-supervised learning. It enforces the classification rule to be smooth with respect to the data-manifold. Here, we deriv…
Nuclear Discrepancy for Active Learning
Tom J. Viering, Jesse H. Krijthe, Marco Loog
Active learning algorithms propose which unlabeled objects should be queried for their labels to improve a predictive model the most. We study active learners that minimize general…