no code implementations • 9 Apr 2024 • Tom Hanika, Tobias Hille
Dimensionality is an important aspect for analyzing and understanding (high-dimensional) data.
no code implementations • 5 Apr 2024 • Tom Hanika, Robert Jäschke
However, the distribution of the data sets poses a problem for the sustainable development of the research field.
no code implementations • 13 Mar 2024 • Tobias Hille, Maximilian Stubbemann, Tom Hanika
Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years.
no code implementations • 6 Mar 2024 • Johannes Hirth, Tom Hanika
We introduce and demonstrate the applicability of our approach based on a topic model derived from a corpus of scientific papers taken from 32 top machine learning venues.
no code implementations • 13 Jul 2023 • Gerd Stumme, Dominik Dürrschnabel, Tom Hanika
One reason for this is the limited availability of computational resources in the last century that would have been required for ordinal computations.
no code implementations • 17 Apr 2023 • Johannes Hirth, Viktoria Horn, Gerd Stumme, Tom Hanika
Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales.
no code implementations • 10 Apr 2023 • Johannes Hirth, Viktoria Horn, Gerd Stumme, Tom Hanika
Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge.
1 code implementation • 5 Apr 2023 • Maximilian Stubbemann, Tobias Hille, Tom Hanika
Real-world datasets are often of high dimension and effected by the curse of dimensionality.
no code implementations • 17 Feb 2023 • Bernhard Ganter, Tom Hanika, Johannes Hirth
Conceptual Scaling is a useful standard tool in Formal Concept Analysis and beyond.
no code implementations • 10 Feb 2023 • Tom Hanika, Johannes Hirth
Random Forests and related tree-based methods are popular for supervised learning from table based data.
1 code implementation • 18 Nov 2022 • Dominik Dürrschnabel, Tom Hanika, Gerd Stumme
Induced bipartite subgraphs of maximal vertex cardinality are an essential concept for the analysis of graphs.
1 code implementation • 11 Oct 2022 • Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider
In the present work, we derive a computationally feasible method for determining said axiomatic ID functions.
1 code implementation • 27 Sep 2022 • Johannes Hirth, Tom Hanika
Explaining neural network models is a challenging task that remains unsolved in its entirety to this day.
no code implementations • 16 Jun 2022 • Bastian Schäfermeier, Johannes Hirth, Tom Hanika
Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows.
no code implementations • 25 Apr 2022 • Bastian Schäfermeier, Gerd Stumme, Tom Hanika
Hence, we propose a principled approach for \emph{mapping research trajectories}, which is applicable to all kinds of scientific entities that can be represented by sets of published papers.
no code implementations • 21 Sep 2021 • Bastian Schäfermeier, Gerd Stumme, Tom Hanika
Selecting the best scientific venue (i. e., conference/journal) for the submission of a research article constitutes a multifaceted challenge.
no code implementations • 17 Jun 2021 • Bastian Schaefermeier, Gerd Stumme, Tom Hanika
The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping.
no code implementations • 12 Jun 2021 • Tom Hanika, Johannes Hirth
Dimension reduction of data sets is a standard problem in the realm of machine learning and knowledge reasoning.
no code implementations • 4 Feb 2021 • Tom Hanika, Johannes Hirth
Measurement is a fundamental building block of numerous scientific models and their creation.
no code implementations • 9 Dec 2020 • Tom Hanika, Johannes Hirth
We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i. e., continuous maps between closure systems, and derive a canonical representation.
no code implementations • 23 Oct 2020 • Bastian Schäfermeier, Gerd Stumme, Tom Hanika
In this task, researchers can be supported by automated publication analysis.
no code implementations • 26 Feb 2020 • Tom Hanika, Johannes Hirth
Knowledge computation tasks are often infeasible for large data sets.
no code implementations • 26 Nov 2019 • Dominik Dürrschnabel, Tom Hanika, Maximilian Stubbemann
Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets.
1 code implementation • 22 Jul 2019 • Maximilian Stubbemann, Tom Hanika, Gerd Stumme
Notably, metric sets of items inclosed in knowledge graphs.
no code implementations • 16 May 2019 • Tom Hanika, Marek Herde, Jochen Kuhn, Jan Marco Leimeister, Paul Lukowicz, Sarah Oeste-Reiß, Albrecht Schmidt, Bernhard Sick, Gerd Stumme, Sven Tomforde, Katharina Anna Zweig
The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life.
no code implementations • 2 Mar 2019 • Dominik Dürrschnabel, Tom Hanika, Gerd Stumme
Concept lattice drawings are an important tool to visualize complex relations in data in a simple manner to human readers.
no code implementations • 3 Feb 2019 • Tom Hanika, Maximilian Marx, Gerd Stumme
Knowledge graphs have recently become the state-of-the-art tool for representing the diverse and complex knowledge of the world.
no code implementations • 20 Dec 2018 • Tom Hanika, Maren Koyda, Gerd Stumme
Computing conceptual structures, like formal concept lattices, is in the age of massive data sets a challenging task.
no code implementations • 28 Sep 2018 • Maximilian Felde, Tom Hanika
We suggest an improved way to randomly generate formal contexts based on Dirichlet distributions.
no code implementations • 19 Sep 2018 • Bastian Schäfermeier, Tom Hanika, Gerd Stumme
For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator.
no code implementations • 16 Jul 2018 • Daniel Borchmann, Tom Hanika, Sergei Obiedkov
We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain.
no code implementations • 15 May 2018 • Tom Hanika, Friedrich Martin Schneider, Gerd Stumme
This work summarizes the first attempt to provide a computationally feasible method for measuring the extent of dimension curse present in a data set with respect to a particular class machine of learning procedures.
no code implementations • 24 Jan 2018 • Tom Hanika, Friedrich Martin Schneider, Gerd Stumme
The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension.
no code implementations • 23 Dec 2017 • Tom Hanika, Jens Zumbrägel
Using notions from combinatorial design theory we further expand those insights as far as providing first results on the decidability problem if a given consortium is able to explore some target domain.
no code implementations • 4 Jan 2017 • Daniel Borchmann, Tom Hanika, Sergei Obiedkov
We revisit the notion of probably approximately correct implication bases from the literature and present a first formulation in the language of formal concept analysis, with the goal to investigate whether such bases represent a suitable substitute for exact implication bases in practical use-cases.