Divergence Based Gaussian Mixture Learning
- Forschungsthema/Bereich
- Artificial Intelligence
- Typ der Abschlussarbeit
- Bachelor / Master
- Startzeitpunkt
- -
- Bewerbungsschluss
- 30.06.2028
- Dauer der Arbeit
- 4 months(BSc) - 6 months(MSc)
Beschreibung
Distances and divergences are crucial for understanding and working with Gaussian Mixture Models (GMMs). They quantify how similar or different two GMMs are, enabling tasks like clustering, model comparison, and anomaly detection. Unlike simple metrics, divergences such as Kullback-Leibler (KL) or Wasserstein distances capture the structure of probabilistic distributions, accounting for both mean and covariance differences. These measures are essential for optimizing GMM parameters, evaluating convergence, and performing model selection. Accurate distance calculations also support applications in signal processing, computer vision, and machine learning, where nuanced distinctions between data distributions are vital for performance and interpretability.Voraussetzung
- Voraussetzungen an Studierende
-
- There are no hard constraints but the more programming and math you know the more you can have fun while doing the project.
- Studiengangsbereiche
-
- Ingenieurwissenschaften
Informatik
- Ingenieurwissenschaften
Betreuung
- Titel, Vorname, Name
- Ali Darijani
- Organisationseinheit
- Computer Science(IAR/IES)
- E-Mail Adresse
- ali.darijani@iosb.fraunhofer.de
- Link zur eigenen Homepage/Personenseite
- Website
Bewerbung per E-Mail
- Bewerbungsunterlagen
-
E-Mail Adresse für die Bewerbung
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an ali.darijani@iosb.fraunhofer.de
Zurück