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Termin (Alle Termine)
Datum Freitag, 12. August 2022
Uhrzeit 11:30 – 12:00 Uhr (Dauer: 30 min)
Ort Raum 348 (Gebäude 50.34)
Vorheriger Termin Fr 22. Juli 2022
Nächster Termin Fr 19. August 2022

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Vortragende(r) Maximilian Georg
Titel A Comparative Analysis of Data-Efficient Dependency Estimators
Vortragstyp Bachelorarbeit
Betreuer(in) Bela Böhnke
Vortragsmodus online
Kurzfassung Dependency estimation is a significant part of knowledge

discovery and allows strategic decisions based on this information. Many dependency estimation algorithms require a large amount of data for a good estimation. But data can be expensive, as an example experiments in material sciences, consume material and take time and energy. As we have the challenge of expensive data collection, algorithms need to be data efficient. But there is a trade-off between the amount of data and the quality of the estimation. With a lack of data comes an uncertainty of the estimation. However, the algorithms do not always quantify this uncertainty. As a result, we do not know if we can rely on the estimation or if we need more data for an accurate estimation. In this bachelor’s thesis we compare different state-of-the-art dependency estimation algorithms using a list of criteria addressing the above-mentioned challenges. We partly developed the criteria our self as well as took them from relevant publications. Many of the existing criteria where only formulated qualitative, part of this thesis is to make these criteria measurable quantitative, where possible, and come up with a systematic approach of comparison for the rest. We also conduct a quantitative analysis of the dependency estimation algorithms by experiment on well-established and representative data sets that performed well in the qualitative analysis.

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