Sqn2Vec: learning sequence representation via sequential patterns with a gap constraint

Nguyen, Dang Pham Hai, Luo, Wei, Nguyen, Tu Dinh, Venkatesh, Svetha and Phung, Quoc-Dinh 2019, Sqn2Vec: learning sequence representation via sequential patterns with a gap constraint, in ECML-PKDD 2018 : Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Springer, Cham, Switzerland, pp. 569-584, doi: 10.1007/978-3-030-10928-8_34.

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Title Sqn2Vec: learning sequence representation via sequential patterns with a gap constraint
Author(s) Nguyen, Dang Pham Hai
Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Nguyen, Tu Dinh
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247
Conference name European Machine Learning and Data Mining. Conference (2018 : Dublin, Ireland)
Conference location Dublin, Ireland
Conference dates 2018/09/10 - 2018/09/14
Title of proceedings ECML-PKDD 2018 : Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Editor(s) Berlingerio, Michele
Bonchi, Francesco
Gärtner, Thomas
Hurley, Neil
Ifrim, Georgina
Publication date 2019
Series European Machine Learning and Data Mining Conference
Start page 569
End page 584
Total pages 16
Publisher Springer
Place of publication Cham, Switzerland
Keyword(s) Sqn2Vec
sequence representations
data sparsity
high-dimensionality problems
sequential patterns (SPs)
ISBN 9783030109271
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-030-10928-8_34
Indigenous content off
Field of Research 08 Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2019, Springer Nature Switzerland AG
Persistent URL http://hdl.handle.net/10536/DRO/DU:30120921

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