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Machine Learning and Data Mining for Sports Analytics - 7th International Workshop, MLSA 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings (Paperback, 1st ed. 2020) Loot Price: R1,521
Discovery Miles 15 210
Repayment Terms: R143 pm x 12*
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Machine Learning and Data Mining for Sports Analytics - 7th International Workshop, MLSA 2020, Co-located with ECML/PKDD 2020,...

Machine Learning and Data Mining for Sports Analytics - 7th International Workshop, MLSA 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings (Paperback, 1st ed. 2020)

Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann

Series: Communications in Computer and Information Science, 1324

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Loot Price R1,521 Discovery Miles 15 210 | Repayment Terms: R143 pm x 12*

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This book constitutes the refereed post-conference proceedings of the 7th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2020, colocated with ECML/PKDD 2020, in Ghent, Belgium, in September 2020. Due to the COVID-19 pandemic the conference was held online. The 11 papers presented were carefully reviewed and selected from 22 submissions. The papers present a variety of topics within the area of sports analytics, including tactical analysis, outcome predictions, data acquisition, performance optimization, and player evaluation.

General

Imprint: Springer Nature Switzerland AG
Country of origin: Switzerland
Series: Communications in Computer and Information Science, 1324
Release date: December 2020
First published: 2020
Editors: Ulf Brefeld • Jesse Davis • Jan Van Haaren • Albrecht Zimmermann
Dimensions: 235 x 155mm (L x W)
Format: Paperback
Pages: 141
Edition: 1st ed. 2020
ISBN-13: 978-3-03-064911-1
Categories: Books > Computing & IT > Applications of computing > Databases > Data mining
Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
Books > Sport & Leisure > Sports & outdoor recreation > Sports training & coaching > General
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LSN: 3-03-064911-3
Barcode: 9783030649111

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