Neural-Based Outlier Detection in Data Streams: Unterschied zwischen den Versionen

Aktuelle Version vom 17. Januar 2018, 11:41 Uhr

Vortragende(r)	Florian Pieper
Vortragstyp	Proposal
Betreuer(in)	Edouard Fouché
Termin	Fr 19. Januar 2018
Vortragssprache
Vortragsmodus
Kurzfassung	Outlier detection often needs to be done unsupervised with high dimensional data in data streams. “Deep structured energy-based models” (DSEBM) and “Variational Denoising Autoencoder” (VDA) 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.

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 |betreuer=Edouard Fouché
 |termin=Institutsseminar/2018-01-19
-|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.
+|kurzfassung=Outlier detection often needs to be done unsupervised with high dimensional data in data streams. “Deep structured energy-based models” (DSEBM) and “Variational Denoising Autoencoder” (VDA) 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.
 }}