https://sdq.kastel.kit.edu/index.php?title=Statistical_Generation_of_High_Dimensional_Data_Streams_with_Complex_Dependencies&feed=atom&action=historyStatistical Generation of High Dimensional Data Streams with Complex Dependencies - Versionsgeschichte2024-03-28T18:28:59ZVersionsgeschichte dieser Seite in SDQ-InstitutsseminarMediaWiki 1.39.6https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Statistical_Generation_of_High_Dimensional_Data_Streams_with_Complex_Dependencies&diff=821&oldid=prevUneft@student.kit.edu am 14. Dezember 2018 um 07:35 Uhr2018-12-14T07:35:29Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Version vom 14. Dezember 2018, 08:35 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/2018-12-14 Zusatztermin</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/2018-12-14 Zusatztermin</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=The evaluation of data stream mining algorithms is an important task in current research. The lack of a ground truth data corpus that covers a large number of desireable features (especially concept drift and outlier placement) is the reason why researchers resort to producing their own synthetic data. This thesis proposes a novel framework ("streamgenerator") that allows to create data streams with finely controlled characteristics. The focus of this work is the conceptualization of the framework, however a prototypical implementation is provided as well. We evaluate the framework by testing <del style="font-weight: bold; text-decoration: none;">out </del>data streams against state-of-the-art dependency measures and outlier detection algorithms.</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=The evaluation of data stream mining algorithms is an important task in current research. The lack of a ground truth data corpus that covers a large number of desireable features (especially concept drift and outlier placement) is the reason why researchers resort to producing their own synthetic data. This thesis proposes a novel framework ("streamgenerator") that allows to create data streams with finely controlled characteristics. The focus of this work is the conceptualization of the framework, however a prototypical implementation is provided as well. We evaluate the framework by testing <ins style="font-weight: bold; text-decoration: none;">our </ins>data streams against state-of-the-art dependency measures and outlier detection algorithms.</div></td></tr>
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</table>Uneft@student.kit.eduhttps://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Statistical_Generation_of_High_Dimensional_Data_Streams_with_Complex_Dependencies&diff=816&oldid=prevUneft@student.kit.edu: Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Alexander Poth |email=alexander.poth@student.kit.edu |vortragstyp=Bachelorarbeit |betreuer=Edouard Fouché |termin=Institutsseminar/201…“2018-12-10T08:57:48Z<p>Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Alexander Poth |email=alexander.poth@student.kit.edu |vortragstyp=Bachelorarbeit |betreuer=Edouard Fouché |termin=Institutsseminar/201…“</p>
<p><b>Neue Seite</b></p><div>{{Vortrag<br />
|vortragender=Alexander Poth<br />
|email=alexander.poth@student.kit.edu<br />
|vortragstyp=Bachelorarbeit<br />
|betreuer=Edouard Fouché<br />
|termin=Institutsseminar/2018-12-14 Zusatztermin<br />
|kurzfassung=The evaluation of data stream mining algorithms is an important task in current research. The lack of a ground truth data corpus that covers a large number of desireable features (especially concept drift and outlier placement) is the reason why researchers resort to producing their own synthetic data. This thesis proposes a novel framework ("streamgenerator") that allows to create data streams with finely controlled characteristics. The focus of this work is the conceptualization of the framework, however a prototypical implementation is provided as well. We evaluate the framework by testing out data streams against state-of-the-art dependency measures and outlier detection algorithms.<br />
}}</div>Uneft@student.kit.edu