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Publications/duluard.22.mlsa - Revision history
2024-03-29T10:34:35Z
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Bot at 17:07, 7 April 2023
2023-04-07T17:07:11Z
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<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> further improvements of our discoveries<nowiki>}</nowiki></div></td>
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https://www.lrde.epita.fr/index.php?title=Publications/duluard.22.mlsa&diff=128119&oldid=prev
Bot at 11:02, 9 December 2022
2022-12-09T11:02:16Z
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 11:02, 9 December 2022</td>
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Bot
https://www.lrde.epita.fr/index.php?title=Publications/duluard.22.mlsa&diff=127598&oldid=prev
Bot: Created page with "{{Publication | published = true | date = 2022-09-19 | title = Discovering and Visualizing Tactics in Table Tennis Games Based on Subgroup Discovery | authors = Pierre Duluard..."
2022-07-15T05:02:17Z
<p>Created page with "{{Publication | published = true | date = 2022-09-19 | title = Discovering and Visualizing Tactics in Table Tennis Games Based on Subgroup Discovery | authors = Pierre Duluard..."</p>
<p><b>New page</b></p><div>{{Publication<br />
| published = true<br />
| date = 2022-09-19<br />
| title = Discovering and Visualizing Tactics in Table Tennis Games Based on Subgroup Discovery<br />
| authors = Pierre Duluard, Xinqing Li, Marc Plantevit, Céline Robardet, Romain Vuillemot<br />
| booktitle = Machine Learning and Data Mining for Sports Analytics - 9th International Workshop, MLSA 2022<br />
| note = Workshop co-located with ECMLPKDD'22<br />
| lrdekeywords = IA<br />
| lrdenewsdate = 2022-09-19<br />
| abstract = We report on preliminary results to automatically identify efficient tactics of elite players in table tennis games. We define such tactics as subgroups of winning strokes which table tennis experts sought to obtain to train players and adapt their strategy during games. We first report on the creation of such subgroups and their ranking by weighted relative accuracy measure (WRAcc). We then report on representation of the subgroups using visualizations that enabled our expert to provide rapid feedback and hence provided us with guidance towards further improvements of our discoveries<br />
| type = inproceedings<br />
| id = duluard.22.mlsa<br />
| bibtex = <br />
@InProceedings<nowiki>{</nowiki> duluard.22.mlsa,<br />
title = <nowiki>{</nowiki>Discovering and Visualizing Tactics in Table Tennis Games<br />
Based on Subgroup Discovery<nowiki>}</nowiki>,<br />
author = <nowiki>{</nowiki>Pierre Duluard and Xinqing Li and Marc Plantevit and<br />
C\'eline Robardet and Romain Vuillemot<nowiki>}</nowiki>,<br />
booktitle = <nowiki>{</nowiki>Machine Learning and Data Mining for Sports Analytics -<br />
9th International Workshop, MLSA 2022<nowiki>}</nowiki>,<br />
year = <nowiki>{</nowiki>2022<nowiki>}</nowiki>,<br />
note = <nowiki>{</nowiki>Workshop co-located with ECMLPKDD'22<nowiki>}</nowiki>,<br />
abstract = <nowiki>{</nowiki>We report on preliminary results to automatically identify<br />
efficient tactics of elite players in table tennis games.<br />
We define such tactics as subgroups of winning strokes<br />
which table tennis experts sought to obtain to train<br />
players and adapt their strategy during games. We first<br />
report on the creation of such subgroups and their ranking<br />
by weighted relative accuracy measure (WRAcc). We then<br />
report on representation of the subgroups using<br />
visualizations that enabled our expert to provide rapid<br />
feedback and hence provided us with guidance towards<br />
further improvements of our discoveries<nowiki>}</nowiki><br />
<nowiki>}</nowiki><br />
<br />
}}</div>
Bot