no code implementations • 7 Dec 2021 • Sebastian Buschjäger, Sibylle Hess, Katharina Morik
Among the most successful online learning methods are Decision Tree (DT) ensembles.
no code implementations • 7 Jan 2020 • Sibylle Hess, Wouter Duivesteijn, Decebal Mocanu
We formally prove that networks with a small Lipschitz modulus (which corresponds to a low susceptibility to adversarial attacks) map data points closer to the cluster centroids, which results in a mapping to a k-means-friendly space.
no code implementations • 4 Jul 2019 • Sibylle Hess, Wouter Duivesteijn
In this paper, we strive to determine the number of clusters by answering a simple question: given two clusters, is it likely that they jointly stem from a single distribution?
no code implementations • 1 Jul 2019 • Sibylle Hess, Nico Piatkowski, Katharina Morik
The Boolean product is a disjunction of rank-1 binary matrices, each describing a feature-relation, called pattern, for a group of samples.
no code implementations • 1 Jul 2019 • Sibylle Hess, Wouter Duivesteijn, Philipp Honysz, Katharina Morik
When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density.
no code implementations • 17 Jun 2019 • Sibylle Hess, Katharina Morik, Nico Piatkowski
In contrast to existing work, the new algorithm minimizes the description length of the resulting factorization.
no code implementations • 17 Jun 2019 • Sibylle Hess, Katharina Morik
Given labeled data represented by a binary matrix, we consider the task to derive a Boolean matrix factorization which identifies commonalities and specifications among the classes.