1 citations · 1 across the 4 of their papers we have counts for
4 papers
The data-driven extreme value distribution: non-parametric tail estimation with a derived stability criterion
Michael Sandbichler, Tobias Hell
Quantifying the likelihood of extreme events underpins risk assessment, yet classical Extreme Value Theory relies on asymptotic assumptions that fail in the data-sparse, non-statio…
Data driven extreme value distribution estimation: Derivation of the Mean Integrated Squared Error, optimal bandwidth selection and stability conditions
Michael Sandbichler, Tobias Hell
We introduce the data driven extreme value distribution (DDEVD) estimator, a kernel-based method for estimating extreme value distributions from data. We derive its mean integrated…
Rule by Rule: Learning with Confidence through Vocabulary Expansion
Albert Nössig, Tobias Hell, Georg Moser
In this paper, we present an innovative iterative approach to rule learning specifically designed for (but not limited to) text-based data. Our method focuses on progressively expa…
A Voting Approach for Explainable Classification with Rule Learning
Albert Nössig, Tobias Hell, Georg Moser
State-of-the-art results in typical classification tasks are mostly achieved by unexplainable machine learning methods, like deep neural networks, for instance. Contrarily, in this…