16 citations · 45 across the 5 of their papers we have counts for
5 papers
Supervised Learning in the Presence of Concept Drift: A modelling framework
Michiel Straat, Fthi Abadi, Zhuoyun Kan +3
We present a modelling framework for the investigation of supervised learning in non-stationary environments. Specifically, we model two example types of learning systems: prototyp…
When can unlabeled data improve the learning rate?
Christina Göpfert, Shai Ben-David, Olivier Bousquet +3
In semi-supervised classification, one is given access both to labeled and unlabeled data. As unlabeled data is typically cheaper to acquire than labeled data, this setup becomes a…
Prototype-based classifiers in the presence of concept drift: A modelling framework
Michael Biehl, Fthi Abadi, Christina Göpfert +1
We present a modelling framework for the investigation of prototype-based classifiers in non-stationary environments. Specifically, we study Learning Vector Quantization (LVQ) syst…
FRI -- Feature Relevance Intervals for Interpretable and Interactive Data Exploration
Lukas Pfannschmidt, Christina Göpfert, Ursula Neumann +2
Most existing feature selection methods are insufficient for analytic purposes as soon as high dimensional data or redundant sensor signals are dealt with since features can be sel…
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces
Benjamin Paaßen, Christina Göpfert, Barbara Hammer
Graph models are relevant in many fields, such as distributed computing, intelligent tutoring systems or social network analysis. In many cases, such models need to take changes in…