16 citations · 16 across the 1 of their papers we have counts for
4 papers · 1 filter
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…