Hierarchical semi-Markov conditional random fields for deep recursive sequential data Tran, Truyen, Phung, Dinh, Bui, Hung and Venkatesh, Svetha 2017, Hierarchical semi-Markov conditional random fields for deep recursive sequential data, Artificial intelligence, vol. 246, pp. 53-85, doi: 10.1016/j.artint.2017.02.003. Attached Files Name Description MIMEType Size Downloads Title Hierarchical semi-Markov conditional random fields for deep recursive sequential data Author(s) Tran, Truyen orcid.org/0000-0001-6531-8907 Phung, Dinh orcid.org/0000-0002-9977-8247 Bui, Hung Venkatesh, Svetha Journal name Artificial intelligence Volume number 246 Start page 53 End page 85 Total pages 33 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2017-05 ISSN 0004-3702 Keyword(s) deep nested sequential processes hierarchical semi-Markov conditional random field partial labelling constrained inference numerical scaling Summary We present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of linear-chain conditional random fields to model deep nested Markov processes. It is parameterised as a conditional log-linear model and has polynomial time algorithms for learning and inference. We derive algorithms for partially-supervised learning and constrained inference. We develop numerical scaling procedures that handle the overflow problem. We show that when depth is two, the HSCRF can be reduced to the semi-Markov conditional random fields. Finally, we demonstrate the HSCRF on two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. The HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases. Language eng DOI 10.1016/j.artint.2017.02.003 Field of Research 0801 Artificial Intelligence And Image Processing 1702 Cognitive Science Socio Economic Objective 0 Not Applicable HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2017, Elsevier Persistent URL http://hdl.handle.net/10536/DRO/DU:30092015 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system 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 Fri, 17 Mar 2017, 09:06:34 EST Sat, 18 Mar 2017, 04:00:30 EST Mon, 20 Mar 2017, 07:11:22 EST Sat, 20 May 2017, 06:03:37 EST Sat, 27 May 2017, 02:30:19 EST Mon, 29 May 2017, 09:49:07 EST Mon, 29 May 2017, 09:52:17 EST Mon, 29 May 2017, 09:52:53 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 51 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Fri, 17 Mar 2017, 09:06:34 EST

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.