20 citations · 22 across the 15 of their papers we have counts for
5 papers · 1 filter
Reciprocal Learning
Julian Rodemann, Christoph Jansen, Georg Schollmeyer
We demonstrate that a wide array of machine learning algorithms are specific instances of one single paradigm: reciprocal learning. These instances range from active learning over…
Statistical Multicriteria Benchmarking via the GSD-Front
Christoph Jansen, Georg Schollmeyer, Julian Rodemann +2
Given the vast number of classifiers that have been (and continue to be) proposed, reliable methods for comparing them are becoming increasingly important. The desire for reliabili…
Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement
Christoph Jansen, Georg Schollmeyer, Hannah Blocher +2
Spaces with locally varying scale of measurement, like multidimensional structures with differently scaled dimensions, are pretty common in statistics and machine learning. Neverth…
In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning
Julian Rodemann, Christoph Jansen, Georg Schollmeyer +1
Self-training is a simple yet effective method within semi-supervised learning. The idea is to iteratively enhance training data by adding pseudo-labeled data. Its generalization p…
Statistical Comparisons of Classifiers by Generalized Stochastic Dominance
Christoph Jansen, Malte Nalenz, Georg Schollmeyer +1
Although being a crucial question for the development of machine learning algorithms, there is still no consensus on how to compare classifiers over multiple data sets with respect…