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. Attached Files Name Description MIMEType Size Downloads Title Sqn2Vec: learning sequence representation via sequential patterns with a gap constraint Author(s) Nguyen, Dang Pham Hai Luo, Wei orcid.org/0000-0002-4711-7543 Nguyen, Tu Dinh Venkatesh, Svetha orcid.org/0000-0001-8675-6631 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 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 Document type: Conference Paper Collection: Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 15 Apr 2019, 14:45:09 EST Tue, 16 Apr 2019, 05:01:33 EST Tue, 16 Apr 2019, 06:05:03 EST Tue, 16 Apr 2019, 14:49:59 EST Tue, 16 Apr 2019, 14:52:56 EST Tue, 16 Apr 2019, 14:53:30 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 2 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Mon, 15 Apr 2019, 14:45:09 EST

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