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Hierarchical semi-markov conditional random fields for recursive sequential data

Truyen, Tran The, Phung, Dinh Q., Bui, Hung H. and Venkatesh, Svetha 2008, Hierarchical semi-markov conditional random fields for recursive sequential data, in NIPS 2008 : Advances in Neural Information Processing Systems 21 : Proceedings of the 2008 Conference, Curran Associates, Red Hook, N. Y., pp. 1657-1664.

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Title Hierarchical semi-markov conditional random fields for recursive sequential data
Author(s) Truyen, Tran TheORCID iD for Truyen, Tran The orcid.org/0000-0001-6531-8907
Phung, Dinh Q.ORCID iD for Phung, Dinh Q. orcid.org/0000-0002-9977-8247
Bui, Hung H.
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Advances in Neural Information Processing. Conference (22nd : 2008 : Vancouver, B. C.)
Conference location Vancouver, B. C.
Conference dates 8-11 Dec. 2008
Title of proceedings NIPS 2008 : Advances in Neural Information Processing Systems 21 : Proceedings of the 2008 Conference
Editor(s) Koller, Daphne
Bengio, Yoshua
Schuurmans, Dale
Bottou, Leon
Culotta, Aron
Publication date 2008
Conference series Advances in Neural Information Processing. Conference
Start page 1657
End page 1664
Total pages 8
Publisher Curran Associates
Place of publication Red Hook, N. Y.
Keyword(s) efficient algorithm
generalisation
hierarchical model
human activities
indoor surveillance
model complexes
observed data
polynomial-time algorithms
reasonable accuracy
semi-Markov
sequential data
Summary Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov chains to model complex hierarchical, nested Markov processes. It is parameterised in a discriminative framework and has polynomial time algorithms for learning and inference. Importantly, we develop efficient algorithms for learning and constrained inference in a partially-supervised setting, which is important issue in practice where labels can only be obtained sparsely. We demonstrate the HSCRF in two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. We show that the HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.
ISBN 9781605609492
1605609498
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2009, Curran Associates
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044754

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.