https://sdq.kastel.kit.edu/index.php?title=Neural-Based_Outlier_Detection_in_Data_Streams&feed=atom&action=historyNeural-Based Outlier Detection in Data Streams - Versionsgeschichte2024-03-28T23:05:25ZVersionsgeschichte dieser Seite in SDQ-InstitutsseminarMediaWiki 1.39.6https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Neural-Based_Outlier_Detection_in_Data_Streams&diff=542&oldid=prevUgcyw@student.kit.edu am 17. Januar 2018 um 10:41 Uhr2018-01-17T10:41:23Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Version vom 17. Januar 2018, 11:41 Uhr</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|betreuer=Edouard Fouché</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|betreuer=Edouard Fouché</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|termin=Institutsseminar/2018-01-19</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|termin=Institutsseminar/2018-01-19</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>|kurzfassung=Outlier detection often needs to be done unsupervised with high dimensional data in data streams. “Deep structured energy-based models” (DSEBM) and <del style="font-weight: bold; text-decoration: none;">“Structured </del>Denoising Autoencoder” (<del style="font-weight: bold; text-decoration: none;">SDA</del>) 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.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>|kurzfassung=Outlier detection often needs to be done unsupervised with high dimensional data in data streams. “Deep structured energy-based models” (DSEBM) and <ins style="font-weight: bold; text-decoration: none;">“Variational </ins>Denoising Autoencoder” (<ins style="font-weight: bold; text-decoration: none;">VDA</ins>) 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.</div></td></tr>
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</table>Ugcyw@student.kit.eduhttps://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Neural-Based_Outlier_Detection_in_Data_Streams&diff=541&oldid=prevUgcyw@student.kit.edu: Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Florian Pieper |email=florian@aipieper.de |vortragstyp=Proposal |betreuer=Edouard Fouché |termin=Institutsseminar/2018-01-19 |kurzfass…“2018-01-16T11:53:43Z<p>Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Florian Pieper |email=florian@aipieper.de |vortragstyp=Proposal |betreuer=Edouard Fouché |termin=Institutsseminar/2018-01-19 |kurzfass…“</p>
<p><b>Neue Seite</b></p><div>{{Vortrag<br />
|vortragender=Florian Pieper<br />
|email=florian@aipieper.de<br />
|vortragstyp=Proposal<br />
|betreuer=Edouard Fouché<br />
|termin=Institutsseminar/2018-01-19<br />
|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.<br />
}}</div>Ugcyw@student.kit.edu