Neural-Based Outlier Detection in Data Streams
| Vortragende(r) | Florian Pieper | |
|---|---|---|
| Vortragstyp | Proposal | |
| Betreuer(in) | Edouard Fouché | |
| Termin | Fr 19. Januar 2018 | |
| Vortragsmodus | ||
| Kurzfassung | Outlier detection often needs to be done unsupervised with high dimensional data in data streams. “Deep structured energy-based models” (DSEBM) and “Structured Denoising Autoencoder” (SDA) are two promising approaches for outlier detection. They will be implemented and adapted for usage in data streams. Finally, their performance will be shown in experiments including the comparison with state of the art approaches. | |