Institutsseminar/2017-10-06: Unterschied zwischen den Versionen
Zur Navigation springen
Zur Suche springen
(Die Seite wurde neu angelegt: „{{Termin |datum=2017/10/06 |raum=Raum 348 (Gebäude 50.34) }}“) |
|||
Zeile 1: | Zeile 1: | ||
{{Termin | {{Termin | ||
− | |datum=2017/10/06 | + | |datum=2017/10/06 11:30:00 |
|raum=Raum 348 (Gebäude 50.34) | |raum=Raum 348 (Gebäude 50.34) | ||
}} | }} |
Aktuelle Version vom 8. August 2017, 13:55 Uhr
Datum | Fr 6. Oktober 2017, 11:30 Uhr | |
---|---|---|
Dauer | 45 min | |
Ort | Raum 348 (Gebäude 50.34) | |
Webkonferenz | ||
Vorheriger Termin | Fr 29. September 2017 | |
Nächster Termin | Fr 13. Oktober 2017 |
Vorträge
Vortragende(r) | Daniel Popovic | |
---|---|---|
Titel | High-Dimensional Neural-Based Outlier Detection | |
Vortragstyp | Diplomarbeit | |
Betreuer(in) | Edouard Fouché | |
Vortragsmodus | ||
Kurzfassung | Outlier detection in high-dimensional spaces is a challenging task because of consequences of the curse of dimensionality. Neural networks have recently gained in popularity for a wide range of applications due to the availability of computational power and large training data sets. Several studies examine the application of different neural network models, such an autoencoder, self-organising maps and restricted Boltzmann machines, for outlier detection in mainly low-dimensional data sets. In this diploma thesis we investigate if these neural network models can scale to high-dimensional spaces, adapt the useful neural network-based algorithms to the task of high-dimensional outlier detection, examine data-driven parameter selection strategies for these algorithms, develop suitable outlier score metrics for these models and investigate the possibility of identifying the outlying dimensions for detected outliers. |
- Neuen Vortrag erstellen