5 citations · 10 across the 5 of their papers we have counts for
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cs.LG2019★ 5 cited
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information
Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder +3
Advances in machine learning technologies have led to increasingly powerful models in particular in the context of big data. Yet, many application scenarios demand for robustly int…
cs.LG2019★ 1 cited
Feature Relevance Bounds for Ordinal Regression
Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl +2
The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i.e. the prediction of ordered classes. Besides model…