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Multipleaspect Analysis Of Semantic Trajectories First International Workshop Master 2019 Held In Conjunction With Ecmlpkdd 2019 Wrzburg Germany September 16 2019 Proceedings Konstantinos Tserpes Chiara Renso Stan Matwin Konstantinos Tserpes

  • SKU: BELL-28687232
Multipleaspect Analysis Of Semantic Trajectories First International Workshop Master 2019 Held In Conjunction With Ecmlpkdd 2019 Wrzburg Germany September 16 2019 Proceedings Konstantinos Tserpes Chiara Renso Stan Matwin Konstantinos Tserpes
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Multipleaspect Analysis Of Semantic Trajectories First International Workshop Master 2019 Held In Conjunction With Ecmlpkdd 2019 Wrzburg Germany September 16 2019 Proceedings Konstantinos Tserpes Chiara Renso Stan Matwin Konstantinos Tserpes instant download after payment.

Publisher: Springer
File Extension: EPUB
File size: 6.41 MB
Author: Konstantinos Tserpes & Chiara Renso & Stan Matwin [Konstantinos Tserpes]
ISBN: 9783030380809, 9783030380816, 3030380807, 3030380815
Language: English
Year: 2020

Product desciption

Multipleaspect Analysis Of Semantic Trajectories First International Workshop Master 2019 Held In Conjunction With Ecmlpkdd 2019 Wrzburg Germany September 16 2019 Proceedings Konstantinos Tserpes Chiara Renso Stan Matwin Konstantinos Tserpes by Konstantinos Tserpes & Chiara Renso & Stan Matwin [konstantinos Tserpes] 9783030380809, 9783030380816, 3030380807, 3030380815 instant download after payment.

This open access book constitutes the refereed post-conference proceedings of the First International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019, held in conjunction with the 19th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, in W�rzburg, Germany, in September 2019.


The 8 full papers presented were carefully reviewed and selected from 12 submissions. They represent an interesting mix of techniques to solve recurrent as well as new problems in the semantic trajectory domain, such as data representation models, data management systems, machine learning approaches for anomaly detection, and common pathways identification.

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