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Eyke Hüllermeier
Researcher at Ludwig Maximilian University of Munich
Publications - 473
Citations - 13894
Eyke Hüllermeier is an academic researcher from Ludwig Maximilian University of Munich. The author has contributed to research in topics: Fuzzy logic & Computer science. The author has an hindex of 57, co-authored 430 publications receiving 11437 citations. Previous affiliations of Eyke Hüllermeier include Otto-von-Guericke University Magdeburg & University of Paderborn.
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Multilabel classification via calibrated label ranking
TL;DR: This work proposes a suitable extension of label ranking that incorporates the calibrated scenario and substantially extends the expressive power of existing approaches and suggests a conceptually novel technique for extending the common learning by pairwise comparison approach to the multilabel scenario, a setting previously not being amenable to the pairwise decomposition technique.
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Label ranking by learning pairwise preferences
TL;DR: This work shows that a simple (weighted) voting strategy minimizes risk with respect to the well-known Spearman rank correlation and compares RPC to existing label ranking methods, which are based on scoring individual labels instead of comparing pairs of labels.
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Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
Eyke Hüllermeier,Willem Waegeman +1 more
TL;DR: This paper provides an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular.
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FURIA: an algorithm for unordered fuzzy rule induction
TL;DR: A novel fuzzy rule-based classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm, which significantly outperforms the original RIPPER, as well as other classifiers such as C4.5, in terms of classification accuracy.
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An Approach to Modelling and Simulation of Uncertain Dynamical Systems
TL;DR: It will be shown that all (reasonable) fuzzy functions can be approximated to any degree of accuracy in this way and an interpretation of fuzzy initial value problems is proposed.